research article feature selection for intelligent...

Research ArticleFeature Selection for Intelligent Firefighting RobotClassification of Fire Smoke and Thermal Reflections UsingThermal Infrared Images

Jong-Hwan Kim1 Seongsik Jo1 and Brian Y Lattimer2

1Mechanical amp Systems Engineering Department Korea Military Academy Seoul Republic of Korea2Mechanical Engineering Department Virginia Tech Blacksburg VA 24060 USA

Correspondence should be addressed to Jong-Hwan Kim jonghwan7028gmailcom

Received 26 March 2016 Revised 5 July 2016 Accepted 3 August 2016

Academic Editor Juan A Corrales

Copyright copy 2016 Jong-Hwan Kim et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefightingrobots By classifying fire smoke and their thermal reflections firefighting robots can assess local conditions decide a properheading and autonomously navigate toward a fire Long-wavelength infrared camera images were used to capture the scenedue to the camerarsquos ability to image through zero visibility smoke This paper analyzes motion and statistical texture featuresacquired from thermal images to discover the suitable features for accurate classification Bayesian classifier is implemented toprobabilistically classify multiple classes and a multiobjective genetic algorithm optimization is performed to investigate theappropriate combination of the features that have the lowest errors and the highest performance The distributions of multiplefeature combinations that have 670 or less error were analyzed and the best solution for the classification of fire and smoke wasidentified

1 Introduction

Intelligent firefighting humanoid robots are actively beingresearched to reduce firefighter injuries and deaths as wellas increase their effectiveness on performing tasks [1ndash5]One task is locating a fire inside of a structure outside therobot field of view (FOV) Fire smoke and their thermalreflections can be clues to determine a heading that willultimately lead the robot to the fire so that it can suppress itHowever research for accurately classifying these clues hasbeen incomplete

2 Previous Features

In conventional fire (andor smoke) detection systems [6 7]in Table 1 temperature ionization and ultraviolet light weremainly used to indicate the presence of a fire andor smokeinside the structure but they can have a long response timein large spaces [8] and do not provide sufficient data for thelocation of fire andor smoke Recently using vision systems

color [9ndash12] motion [13 14] both [8 15ndash17] and texture fea-tures [12 18 19] have been researched to characterize fireor smoke in Table 1 However color features from RGBcamera are not applicable to firefighting robots due to thefact that RGB cameras may operate in the visible to shortwavelength infrared (IR) (less than 1 micron) and are notusable in smoke-filled environments where the visibility hassufficiently decreased [2 14]Motion (eg dynamicalmotionshape changing etc) of the feature can be another clue todetect fire and smoke by characterizing flickering flames andsmoke flow from a stationary vision system However thevision system onboard a robot is moving due to the dynamicsof the robot itself and this causes a large amount of noise thatresults in extensive computation for motion compensationTexture features researched in [12 18 19] were used to identifyfire or smoke The spatial characteristics of textures can beuseful to recognize patterns of fire and smoke by remotesensing and are less influenced by rotationmotion [18]

Long-wavelength infrared cameras similar to the hand-held thermal infrared cameras (TICs) that are typically used

Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 8410731 13 pageshttpdxdoiorg10115520168410731

2 Journal of Sensors

Table 1 Conventional and vision-based features

Type Feature Advantages Disadvantages

Conventional features[6 7]

TemperatureIonizationUV light

(i) Detect presence of fireand smoke [8]

(i) Long response time [8](ii) Unable to providesufficient data for firelocating

Model-basedfeatures

Fourier transform [20]Wavelet transform [9]

(i) Frequency contentanalysis(ii) Flexible analysis of bothspace and frequency [25]

(i) Unable to be spatiallylocalized [25]

Vision-based features

Color (RGB) [9ndash12 26] (i) Fire (red)(ii) Smoke (gray)

(i) RGB camera cannotfunction in smoke-filledenvironments [2 14]

Dynamics [13 14] (motionshape change etc)

(i) Flickering flamesrecognition(ii) Smoke flow detection

(i) Can be influenced bydynamical robot motion(ii) Expensive computationfor motion compensation

Texture [12 18 19 27]

(i) Spatial characteristicsfor pattern recognition(ii) Less influenced byrotation and motion [18]

(i) The higher the ordertexture features the morethe computation

Feature maps [28] (CNNdeep learning)

(i) Superior performance inpattern recognition [29](ii) Once trainedapplicable in real-time

(i) Slow learning speed(ii) GPUs required due toexpensive computation

to aid in firefighting tasks within smoke-filled environments[20ndash22] as well as fire-front and burned-area recognition inremote sensing [23] are used in this research Due to the factthat TICs absorb infrared radiation in the long-wavelengthIR (7ndash14 microns) they are able to image surfaces even indense smoke and zero visibility environments [2 14] Inaddition TIC can provide proper information under localor global darkness for example shadows or darkness causedby damaged lighting Recently thermal images from TIC arestudied to recognize pattern and motion remotely [24] Thecameras will detect hot objects as well as thermal reflectionsoff of surfaces As a result image processing on detectedobjects must be sufficiently robust to discern between desiredobjects and their thermal reflections

This study ultimately leads the shipboard autonomousfirefighting robot (SAFFiR) whose prototype is displayed inFigure 1 to autonomously navigate toward fire outside FOVin indoor fire environments For this the robot needs toidentify clues such as smoke and smoke andfire-reflections byitself to correctly navigate toward the fireHowever the recog-nition of key features has not been fully studied This paperanalyzes appropriate combination of features to accuratelyclassify fire smoke their thermal reflections and other hotobjects using thermal infrared images Large-scale fire testswere conducted to create actual fire environments havingvarious ranges of both temperature and smoke conditionsA long-wavelength IR camera was installed to produce 14-bit thermal images of the fire environment These imageswere used to extract motion and statistical texture featuresin regions of interest (ROI) Bayesian classification wasperformed to probabilistically identify multiple classes inreal-time To identify the best combination of features for

Figure 1 A prototype of the shipboard autonomous firefightingrobot (SAFFiR) Note that the data used in this paper were notacquired from this platform

accurate classification the multiobjective optimization wasimplemented using two objective functions resubstitutionand cross-validation errors

3 Motion and Texture Features

In pattern recognition system the choice of features playsan important role in the performance of classification Both

Journal of Sensors 3

(a) RGB images

(b) Thermal images

Figure 2 (a) RGB images of fire scenes and (b) extracted objectsfrom thermal images with optical flow vectors overlaid

motion and texture features were selected because theywere crucial in the previous study of fire andor smokedetection and also best suitable for the thermal image analysisthat is major information the firefighting robot can acquireunder fire environments Optical flow a popular motionmeasurement was used for the motion features while thefirst- and second-statistical texture features were applied forthe texture measurement

A FLIR A35 long-wavelength IR camera which is capableof imaging through zero visibility environments was usedto produce images All images were from a 320 times 256-pixelfocal plane array 60Hz frame rate that produces 14-bit imageswith an intensity range of minus16384 for minus40∘C to minus1 for 550∘CFifteen features from optical flow and the statistical texturefeatures are evaluated to find the best feature combinationOptical flow shows temporal variations due tomoving objectsin the FOV or motion of the robot The first- and second-order statistical texture features display spatial characteristicsof objects in the scene

31 Motion Features by Optical Flow Optical flow is a usefultool to recognize motion of an object in sequential images[30] It consists of local and global methods Lucas-Kanade(LK) is a local method that is relatively robust with a lessdense flowfield whileHorn-Schunck (HS) is a globalmethodwith a dense flow field and high sensitivity to noise [31]Because the intensities in the thermal image change due tothe varying fire environment LK method that has higherrobustness compared with HS was selected in this researchto measure motion features of the objects Two featuresof optical flow vector number (OFVN) and optical flowmeanmagnitude (OFVMM)were computed to quantitativelycharacterize motions of fire smoke and their reflectionsFigure 2 contains RGB and thermal images of dense smokein a hallway and a wood crib fire in a room Red arrows in the

thermal images indicate the direction and magnitude of theoptical flow vectors with red boxes that show smoke fire andthermal reflections

32 First- and Second-Order Statistical Texture Features Thefirst- and second-order statistical features were considered inthis study for object classification The first-order statisticalfeatures estimate individual property of pixels not character-izing any relationship between neighboring pixels and canbe computed using the intensity histogram of the candidateregion of interest (ROI) in the image As described in [32]mean (MNI) variance (VAR) standard deviation (STD)skewness (SKE) and kurtosis (KUR) were calculated by

MNI = 1

119873119875

119873119875

sum

119894119895=1

119868119894119895

VAR = 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

STD (120590) = ( 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

)

12

SKE = 1

1205903

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

3

KUR = 1

1205904

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

4

(1)

where 119868119894119895

refers to the intensity of a pixel at 119894 and 119895 and119873119875denotes the number of pixels (NOP) of the object in the

image The second-order statistical features represent spatialrelationships between a pixel and its neighbors Gray-levelcooccurrence matrix (GLCM) [33] is used to account foradjacent pixel relationships in four directions (horizontalvertical left and right diagonals) by quantizing the spatialcooccurrence of neighboring pixels A total of seven second-order statistics features were used including dissimilarity(DIS) entropy (ENT) contrast (CON) inverse difference(INV) correlation (COR) uniformity (UNI) and inverse dif-ferencemoment (IDM) Tomeasure these features a normal-ized cooccurrence matrix 119862

119894119895is used which can be defined as

119862119894119895=

119875119894119895

sum119873119866

119894119895=1

119875119894119895

(2)

where 119875119894119895refers to the frequency of occurrences of the gray-

level of adjacent pixels at 119894 and 119895 within the four directionsand 119873

119866denotes the number of the gray-levels in the

quantized image The denominator of (2) normalizes 119875119894119895to

be estimates of the cooccurrence probabilities After buildingthe normalized cooccurrence matrix 119862

119894119895 seven features of

the second-order statistics features were computed by

DIS = sum119862119894119895

1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

ENT = minussum119862119894119895log119862119894119895


CON = sum119862119894119895(119894 minus 119895)

2

INV = sum119862119894119895

1 +1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

COR = sum(119894119895 + 120583

119894120583119895minus 120583119894minus 120583119895) 119862119894119895

120590119894120590119895

UNI = sum1198622119894119895

IDM = sum

119862119894119895

1 + (119894 minus 119895)2

(3)

4 Object Extraction andBayesian Classification

One of the main characteristics of fire smoke and theirthermal reflections in thermal images is that they are higherin intensity than the background With intensity related totemperature in the thermal image higher temperature objectsappear brighter than the background Hence intensity is aprimary factor for object extraction from the backgroundAssuming that the thermal image histogram has a bimodaldistribution for foreground (ie object) and backgroundthe clustering-based image autothresholding method [34]called Otsumethod can calculate an optimum threshold thatseparates objects and background creating a binary imagewith 0 being the background and 1 being the objects Thebinary images were filtered to remove small regions and holesinside objects through morphological filtering techniquesAfter convoluting the original 14-bit image with the filtered-binary image a final image was obtained that includes theoriginal 14-bit intensities in objects as well as zeroes in thebackground

There are several classification methods commonly usedin supervised machine learning 119896-nearest neighbors (119896NN)decision tree (DT) neural networks (NN) support vectormachine (SVM) and Naıve Bayesian For this study theseclassification methods were analyzed by considering threepoints capability to classify multiple classes such as firesmoke and their thermal reflections less chance of overfit-ting problem because under fire environments there couldbe a number of situations that are not learned or trainedreal-time implementation because firefighting robot needs tomake a decision in real-time otherwise it cannot operate itstask 119896NN is insensitive to outliers but it needs a large amountof memory and expensive computation [35] DT has lowcomputation burden but for the multiclasses classificationit may generate a complicated tree structure and may causeoverfitting problem [35 36] NN shows high performancewhen processing with multidimensions and continuous fea-tures but cannot overcome overfitting problem SVM pro-vides fast computation and the highest accuracy but it cannotbe used for the multilabel classification because it producesbinary results [37] Naıve Bayesian classification is Bayesrsquotheorem-based probabilistic classification and is popular for

pattern recognition applications Although this method haslower accuracy compared with other classifiers and assumesthat each feature is independent it has fast computationrobustness to untrained cases and less chance of overfitting[35] In addition this classification has the capability ofprobabilistic decision making over multiple classes with fastcomputation for real-time implementation In this studyBayesian classification is used for evaluation of each feature

With several given features 1198651 1198652 119865

119902 (motion and

texture features) we can calculate the probability that oneclass 119862

ℎ(fire smoke thermal reflections etc) corresponds

to the candidate 119896 by using a conditional probability 119896119901(119862ℎ|

11986511198652sdot sdot sdot 119865119902) also known as the posterior probability By using

Bayesrsquo theorem it can be written with prior likelihood andevidence as shown in

119896

119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902)

=

119896

119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

sum119862ℎ

119896119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

(4)

where 119896119901(119862ℎ) is the prior probability meaning it represents

candidate 119896 probability to be 119862ℎand can be calculated by

number of samples of class 119862ℎdivided by the total number

of samples 119896119891(11986511198652sdot sdot sdot 119865119902| 119862ℎ) is the likelihood function

and the denominator of (4) is the evidence that plays asa normalizing constant by the summation of productionbetween the prior and likelihood at each class By applying theconditional independence assumption the likelihood func-tion can be rewritten by

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ) =

119902

prod

119894=1

119891 (119865119894| 119862ℎ) (5)

The conditional probability density function 119891(119865119894| 119862ℎ) sim

119873(120583119865119894|119862ℎ

Σ119865119894|119862ℎ

) can be described as

119891 (119865119894| 119862ℎ) =

1

radic2120587Σ119865119894|119862ℎ

119890minus(12)(119865119894minus120583119865119894|119862ℎ

)

119879Σ

minus1

119865119894|119862ℎ(119865119894minus120583119865119894|119862ℎ

)

(6)

where

120583119865119894|119862ℎ

= 120583119865119894+ Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

(119862ℎminus 120583119862ℎ)

Σ119865119894|119862ℎ

= Σ119865119894119865119894minus Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

Σ119862ℎ119865119894

(7)

As shown in Table 2 Gaussian parameters for fifteen featureswith respect to smoke smoke thermal reflection fire andfire thermal reflection were estimated by using the maximumlikelihood estimation [38] Probability density distributionsfor the entire features are illustrated in Figure 3 With (5) theevidence and then the posterior probability of each class werecalculated By applying the maximum priority decision rulein (8) the Bayesian classification was used to predict the classand probability of each candidate in the scene

class = argmax119862ℎ

(119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902))

prob = max (119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902))

(8)


Table 2 Gaussian parameters

Smoke Smoke-reflection Fire Fire-reflection120583 Σ 120583 Σ 120583 Σ 120583 Σ

MNI minus12665119864 + 04 69008119864 + 02 minus13383119864 + 04 35543119864 + 02 minus59714119864 + 03 16050119864 + 03 minus71399119864 + 03 46070119864 + 02

VAR 47578119864 + 05 44154119864 + 05 44012119864 + 04 41227119864 + 04 10501119864 + 07 22343119864 + 06 13300119864 + 06 88178119864 + 05

NOP 10733119864 + 03 42287119864 + 03 32220119864 + 01 13676119864 + 02 22174119864 + 02 17105119864 + 03 59575119864 + 01 23911119864 + 02

STD 62146119864 + 02 29931119864 + 02 18314119864 + 02 10236119864 + 02 32170119864 + 03 38930119864 + 02 10534119864 + 03 46992119864 + 02

SKE 11672119864 minus 01 58208119864 minus 01 minus94009119864 minus 02 62857119864 minus 01 22230119864 minus 02 52287119864 minus 01 22385119864 minus 01 71380119864 minus 01

KUR 30045119864 + 00 20213119864 + 00 36553119864 + 00 16131119864 + 00 22283119864 + 00 11517119864 + 00 34848119864 + 00 11912119864 + 00

OFVN 28832119864 + 04 14156119864 + 04 11848119864 + 03 87345119864 + 02 11257119864 + 04 13722119864 + 04 29978119864 + 03 22034119864 + 03

OFVMM 86830119864 + 01 56259119864 + 01 11687119864 + 02 79444119864 + 01 17598119864 + 02 29308119864 + 02 12641119864 + 02 67234119864 + 01

DIS 71791119864 minus 02 17966119864 minus 02 28045119864 minus 02 12245119864 minus 02 10937119864 minus 01 86515119864 minus 02 47308119864 minus 02 28550119864 minus 02

ENT 41949119864 minus 01 93683119864 minus 02 19576119864 minus 01 86106119864 minus 02 33681119864 minus 01 19107119864 minus 01 17115119864 minus 01 99702119864 minus 02

CON 89384119864 minus 01 36871119864 minus 01 12402119864 minus 01 65921119864 minus 02 92956119864 minus 01 69179119864 minus 01 39683119864 minus 01 26996119864 minus 01

IND 98588119864 minus 01 63845119864 minus 03 99646119864 minus 01 15141119864 minus 03 96334119864 minus 01 37231119864 minus 02 98771119864 minus 01 79275119864 minus 03

COR 96636119864 minus 01 31625119864 minus 02 88533119864 minus 01 43467119864 minus 02 89406119864 minus 01 30935119864 minus 02 88417119864 minus 01 57861119864 minus 02

UNI 51044119864 minus 01 20109119864 minus 01 95222119864 minus 01 31669119864 minus 02 67906119864 minus 01 26718119864 minus 01 85305119864 minus 01 10599119864 minus 01

IDM 60061119864 minus 01 19160119864 minus 01 97533119864 minus 01 16844119864 minus 02 77850119864 minus 01 24321119864 minus 01 92043119864 minus 01 58631119864 minus 02

Table 3 The object numbers of smoke smoke-reflection fire fire-reflection and other hot objects classes

Type Total Smoke Smoke-reflection Fire Fire-reflection Other hot objectsNumber of objects 10775 5190 1445 1464 489 2187

Figure 3 shows probability density distribution of eachclass using the Gaussian parameters of Table 2 Gaussiandistribution for classes in Figure 3 shows how fire fire-reflection smoke and smoke-reflection are distributed by thefifteen features Some features split out the distribution of thefour classes while others cause overlap For example MNIbest describes a well split out case of the classes althoughsmoke and its reflection and fire and its reflection do overlapSKE shows the worst case in which all classes overlap makingit impossible to distinguish any of the four classes

5 Result and Discussion

The accuracy in classifying fire objects was analyzed usingdata from a series of large-scale tests in the facility [1]using actual fires up to 75 kW Fires included latex foamwood cribs and propane gas fires from a sand burner Thesedifferent types of fires produced a range of temperature andsmoke conditions Latex foam fires produced lower tempera-ture conditions but dense low visibility smoke Converselypropane gas fires produced higher gas temperatures andlight smoke Wood crib fires resulted in smoke and gastemperatures between those of latex foam and propane gasfires however these fires resulted in sparks created from theburning wood Thermal images were collected by driving awheeled mobile robot through the setup during a fire test Atotal of 10775 objects were collected from the experimentsand categorized as either smoke smoke-reflection fire fire-reflection or other hot objects in order to be served as cluesto lead the firefighting robot to navigate toward the fire source

outside the FOV In addition as each object has sixteen cor-responding data points (fifteen features and a class) the totalnumber of data points used in this paper is 172400The num-bers of each object in this experiment are shown in Table 3

Two types of error criterions (resubstitution and 119896-foldcross-validation errors [39]) were used to measure howeach feature accurately performs in the classification Resub-stitution error takes the entire dataset to compare the actualclasses with the predicted classes by the Bayesian classifica-tion in order to examine how well the actual and predictedclasses match each other When this criterion is used aloneto enhance accuracy the classification can be overfitted tothe training dataset Cross-validation error is advantageousto detect and prevent from overfitting Instead of using theentire dataset cross-validation randomly selects and splitsthe dataset into 119896 partitions of approximately equal size(119896 = 10) to estimate a mean error by comparing betweenthe randomly selected partition and trained results of theremaining partitions

51 Single Feature Performance The performance results ofeach feature are shown in Table 4 The first-order statisticaltexture features MNI VAR and STD produced the lowesterrors while NOP SKE and KUR show the highest Theseresults show that MNI and VAR are beneficial to distinguishfire smoke and thermal reflections while motion features arenot As NOP shows the highest error OFVMM one of themotion features shows the second highest errors comparedwith the other features This is in part attributed to thedynamic motion of the robot ENT and COR second-order


0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y

minus10000 minus8000 minus6000 minus4000minus12000MNI

0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR

minus2 0 2 4 6minus4SKE

0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3

Figure 3 Probability density distributions of each feature

statistical texture features show42sim45error which is higherthan the other second-order features

52 Multiple Feature Combination Performance The errorresults in Table 4 demonstrate that a single feature cannotaccurately classify fire smoke and thermal reflections Thuspossible combination ofmultiple features was considered andanalyzed to find the best combination of the featuresThe totalnumber of all possible combinations that have two or morefeatures is

119873total =119898

sum

119911=2

(

119898

119911

) (9)

where 119898 refers to the total number of features (ie 119898 = 15)and 119911 is the number of features in the combination Basedon all possible combination the multiobjective genetic algo-rithm optimization [40] in the global optimization toolbox ofMATLAB was used to find the best combination of featuresthat has the highest performance in the classification Theobjective functions in the optimization resubstitution and119896-fold cross-validation errors [39] were used to measurehow accurately different feature combinations perform in theclassification

Figure 4 contains a plot of the error associated with themost promising feature combinations The behavioral solu-tion set is defined as feature combinations with less than 7


Table 4 Performance of each feature

Resubstitution error () Cross-validation error ()MNI 237 238VAR 243 243NOP 721 720STD 232 232SKE 527 528KUR 506 506OFVN 395 400OFVMM 586 586DIS 290 290ENT 446 446CON 285 285IND 301 301COR 412 411UNI 345 345IDM 377 377

Behavioral region

Behavioral solution setGeneral set

66 67 68 69 7 71 72 7365Resubstitute error ()

65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)

Figure 4Multiobjective optimization result showing the general setand behavioral solution set (colored region)

error for both objective functions while the general set refersto all other possible feature combinations The behavioralsolution set contains 00061 of all possible feature combi-nations

The occurrence probability of features in the behavioralsolution set is illustrated in Figure 5 In the behavioralsolution set the first-order statistic texture features MNI andSKE always exist while OFVN NOP and OFVMM featuresdo not Both the first-order statistical texture features STDand VAR and the second-order statistical texture featuresCOR ENT andDIS showahigher occurrence comparedwiththe other first- and second-order texture features while KURIDMUNI IND andCON show lower occurrence Note thatdue to the robotrsquos dynamical motion motion features werenot successful and even not included in the top 10 featurecombinations of the behavioral set

1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM

200 400 600 800 1000 120000Occurrence probability ()

Figure 5 Occurrence analysis of the features in the behavioralregion

The top features based on the probability occurrence inFigure 5 are COR ENT DIS SKE STD VAR and MNIHowever the combination of these seven features does notresult in the best solution for classification Table 5 containsthe classification performance of the combination of featuresin the behavioral solution set In order to evaluate the per-formance of each feature combination various performancemeasures have been used such as precision sensitivity F-measure and accuracy Precision measures the fraction ofpositive instances from the group that the classifier predictedto be positive and recall measures the fraction of positiveexamples from the positive group of the actual class and [35]F-measure is the harmonic mean and accuracy is the pro-portion of true resultsThesemeasures can bemathematicallydefined as

Precision = TPTP + FP

Sensitivity = TPTP + FN

119865-Measure =2 (Sensitivity sdot Precision)Sensitivity + Precision

Accuracy = TPTP + FP + FN

(10)

where TP is correctly classified positive cases FP is incor-rectly classified negative cases and FN is incorrectly classifiedpositive cases For the performance measurement confusionmatrixes were created as described in Appendix and appliedinto (10) In the precision index number 1 combinationshows the highest performance in the behavioral solutionset while index number 7 combination shows the lowestIn the sensitivity index number 7 combination records thehighest results while index number 4 does the lowest In theF-measure and accuracy index number 2 combination showsthe highest record while index number 4 does the lowestBased on the confusion matrixes most of misclassificationoccurs in the classification of smoke smoke-reflection andother hot objects because during small fire texture patternsof these classes were diminished and the intensity was too low


Table 5 Results of error case and performance at each feature combination in the behavioral solution set (Resu means resubstitution andCros refers to cross-validation)

Index Combination of features Error () Case Performance ()Resu Cros TP FP FN Precision Sensitivity 119865-measure Accuracy

1 MNI VAR ENT COR SKE 690 689 10049 402 324 9615 9688 9651 93262 MNI DIS COR SKE STD 668 670 10069 459 247 9564 9761 9662 93453 MNI ENT COR SKE STD 676 675 10061 447 267 9575 9741 9657 9337

4 MNI VAR DIS ENT CON CORSKE STD 698 690 9980 454 341 9565 9670 9617 9262

5 MNI VAR DIS ENT COR UNISKE STD 689 690 10037 453 285 9568 9724 9645 9315

6 MNI VAR DIS ENT COR IDMSKE STD 677 679 10047 461 267 9561 9741 9650 9324

7 MNI VAR DIS ENT IDM SKEKUR STD 696 694 10033 505 237 9521 9769 9643 9311

8 MNI VAR DIS ENT IND CORUNI SKE STD 695 699 10029 451 295 9570 9714 9641 9308

9 MNI VAR DIS ENT IND CORIDM SKE STD 688 690 10035 457 283 9564 9726 9644 9313

10 MNI VAR DIS ENT COR IDMSKE KUR STD 689 691 10036 478 261 9545 9747 9645 9314

PrecisionSensitivity

F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)

2 3 4 5 6 7 8 9 101Feature combination index

Figure 6 Results of performance at each feature combination in thebehavioral solution set

to distinguish The best solution was determined to be indexnumber 2 combination of MNI DIS COR SKE and STDwhich has the lowest of resubstitution and cross-validationerrors 668 and 670 respectively This combinationincludes all of the top features based on the probability occur-rence except ENT and VAR The four performance results ateach feature combination in the behavioral solution set areshown in Figure 6 where the highest results aremarked in redcircles and the lowest in green-dot circles Sensitivity appearshigher than precision at each feature combination becauseFPs are larger than FNs in the confusion matrix Particularlyindex number 7 has the biggest difference between FP and FNresulting in the highest sensitivity and lowest precision Thesummation of FP and FN in index number 4 is the highestin the behavioral solution set resulting in the lowest accuracywhile index number 2 has the lowest summation of FP andFN providing the highest accuracy

This study investigated a wide range of features fromlong-wavelength infrared camera images analyzed normaldistributions of fifteen features with respect to the classes ofsmoke fire and their thermal reflections and discovered thehighest performing feature combination by examining singlefeatures and multiple feature combinations As a result theproposed feature combination of MNI DIS COR SKE andSTD increases the performance compared with the previousstudy [1] which used MNI VAR ENT and IDM As shownin Figure 7 the errors are reduced by 286 and 268 resub-stitution and cross-validation errors and performances areincreased by 290 158 020 and 285 accuracy F-measure sensitivity and precision respectively

Figure 8 shows original visual and thermal images withthe robot at three different locations start point hallwayentrance and room entrance described in the experimentalfacility Each row relates to a series of images from the robotat three locations The first row contains visible images ofthe robot view As seen in the visible image at start pointfurther information regarding the hallway is limited due toshadowing of the light The image at hallway entrance showsa smoke layer in the upper portion of the hallway due to afire inside the roomThe image at the room entrance displaysa wood crib fire with sparks Because of soot and relativedifference in brightness the background is shown darker andthus limiting information on the background around the fire

Thermal infrared images are displayed in the second rowto show information that RGB camera cannot provide infire environments Unlike visual image at start point thatis obscured due to shadowing the presence of smoke andits thermal reflections on the ventilation hood can be obvi-ously perceived The red boxes on thermal images indicateobjects extracted through the adaptive object extraction withoptical flows and identification numbers In spite of dense


9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy

Resubstitution error

Cross-validation error

The proposedThe previous

2000 4000 6000 8000 10000000()

Figure 7 Result comparison between the previous and the proposed studies

① Start point ② Hallway entrance ③ Room entrance

IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis

Figure 8 Original visible and IR images with image analysis and classification results at different locations in the test setup of actual fires

smoke-filled and low visibility environments thermal imagescan generate the images of smoke and fire as well as back-ground information that is otherwise not visible throughvisual imaging

On the third row class labels and posterior probabilitiesof each candidate are displayed at the center of candidateROI as a result of Bayesian classification Using enhancedimage processing techniques the thermal images can bemore


050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64

Feature combination index number 2

Feature combination index number 3 Feature combination index number 4


0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD

MNI ENT COR SKE STD MNI VAR DIS ENT CON COR SKE STD

MNI VAR DIS ENT COR UNI SKE STD

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67

MNI VAR DIS ENT COR IDM SKE STD

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63

MNI VAR DIS ENT COR IDM SKE KUR STD

MNI VAR DIS ENT IDM SKE KUR STD

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

MNI VAR DIS ENT IND COR IDM SKE STD

MNI VAR DIS ENT IND COR UNI SKE STD

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

Figure 9 Results of confusion matrix at each feature combination

refined and clearer than the thermal images on the secondrow Smoke fire and their thermal reflections are identifiedand marked in red or orange ellipses

6 Conclusion

The appropriate combination of features was investigated toaccurately classify fire smoke and their thermal reflectionsusing thermal images Gray-scale 14-bit images from a singleinfrared camera were used to extract motion and texture fea-tures by applying a clustering-based autothresholding tech-nique Bayesian classification is performed to probabilisti-cally identify multiple classes during real-time implemen-tation To find the best combination of features a multi-objective genetic algorithm optimization was implementedusing resubstitution and cross-validation errors as objectivefunctions Large-scale fire tests with different fire sources

were conducted to create a range of temperature and smokeconditions to evaluate the feature combinations

Fifteenmotion and texture features were analyzed and theprobability density functions of the features were computedby the maximum likelihood estimation The combinationof multiple features was determined to more accuratelyclassify fire smoke and thermal reflections compared witha single feature In the behavioral solution set where featurecombinations produce less than 7 resubstitution and cross-validation errors COR ENT DIS SKE STD VAR andMNIhad 800 or more occurrence while other features had400 or less occurrence The feature combination of MNIDIS COR SKE and STD produced the highest performancein the classification resulting in 668 and 670 resubstitu-tion and cross-validation errors and 9564 9761 9662and 9345 precision sensitivity F-measure and accuracyrespectively


In the near future the classification of fire smoke andtheir thermal reflections will be evaluated on any classifiersand features to increase performanceThe convolution neuralnetwork of deep learning which has recently shown highperformance could be explored as a classifier also model-based image features such as discrete wavelet transform willbe further studied

Appendix

See Figure 9

Competing Interests

The authors declare that there is no conflict of interestsregarding the publication of this manuscript

Acknowledgments

This work was sponsored by the Office of Naval ResearchGrant no N00014-11-1-0074 scientific office Dr ThomasMcKenna in USA Hwarang-dae Research Institute in Seouland Agency for Defense Development in Daejeon SouthKorea The authors would like to thank Mr Joseph Starr andMr JoshMcNeil for assisting in performing the fire testsTheauthors would also like to thank Rosana K Lee for helpingand supporting this research

References

[1] J-H Kim and B Y Lattimer ldquoReal-time probabilistic classifi-cation of fire and smoke using thermal imagery for intelligentfirefighting robotrdquo Fire Safety Journal vol 72 pp 40ndash49 2015

[2] J W Starr and B Y Lattimer ldquoEvaluation of navigation sensorsin fire smoke environmentsrdquo Fire Technology vol 50 no 6 pp1459ndash1481 2014

[3] J-H Kim B Keller and B Y Lattimer ldquoSensor fusion basedseek-and-find fire algorithm for intelligent firefighting robotrdquoin Proceedings of the IEEEASME International Conference onAdvanced Intelligent Mechatronics (AIM rsquo13) pp 1482ndash1486IEEE Wollongong Australia July 2013

[4] J G McNeil J Starr and B Y Lattimer ldquoAutonomous fire sup-pression using multispectral sensorsrdquo in Proceedings of theIEEEASME International Conference on Advanced IntelligentMechatronics Mechatronics for HumanWellbeing (AIM rsquo13) pp1504ndash1509 Wollongong Austrailia July 2013

[5] J-H Kim J W Starr and B Y Lattimer ldquoFirefighting robotstereo infrared vision and radar sensor fusion for imagingthrough smokerdquo Fire Technology vol 51 no 4 pp 823ndash8452015

[6] RC Luo andK L Su ldquoAutonomous fire-detection systemusingadaptive sensory fusion for intelligent security robotrdquo IEEEASME Transactions on Mechatronics vol 12 no 3 pp 274ndash2812007

[7] M A Jackson and I Robins ldquoGas sensing for fire detectionmeasurements of CO CO

2

H2

O2

and smoke density in Euro-pean standard fire testsrdquo Fire Safety Journal vol 22 no 2 pp181ndash205 1994

[8] B U Toreyin R G Cinbis Y Dedeoglu and A E Cetin ldquoFiredetection in infrared video using wavelet analysisrdquo OpticalEngineering vol 46 no 6 Article ID 067204 2007

[9] B U Toreyin Y Dedeoglu andA E Cetin ldquoWavelet based real-time smoke detection in videordquo in Proceedings of the 13th Euro-pean Signal Processing Conference pp 4ndash8 Antalya TurkeySeptember 2005

[10] T Celik H Demirel H Ozkaramanli and M Uyguroglu ldquoFiredetection using statistical color model in video sequencesrdquoJournal of Visual Communication and Image Representation vol18 no 2 pp 176ndash185 2007

[11] L Merino F Caballero J R Martınez-de-Dios I Maza and AOllero ldquoAn unmanned aircraft system for automatic forest firemonitoring and measurementrdquo Journal of Intelligent amp RoboticSystems vol 65 no 1 pp 533ndash548 2012

[12] Y Wang T W Chua R Chang and N T Pham ldquoReal-timesmoke detection using texture and color featuresrdquo in Proceed-ings of the 21st International Conference on Pattern Recognition(ICPR rsquo12) pp 1727ndash1730 Tsukuba Japan November 2012

[13] G Marbach M Loepfe and T Brupbacher ldquoAn image process-ing technique for fire detection in video imagesrdquo Fire SafetyJournal vol 41 no 4 pp 285ndash289 2006

[14] M I Chacon-Murguia and F J Perez-Vargas ldquoThermal videoanalysis for fire detection using shape regularity and intensitysaturation featuresrdquo in Pattern Recognition J F Martınez-Trinidad J A Carrasco-Ochoa C B-Y Brants and E RHancock Eds vol 6718 of Lecture Notes in Computer Sciencepp 118ndash126 Springer Berlin Germany 2011

[15] W Phillips III M Shah and N D V Lobo ldquoFlame recognitionin videordquo Pattern Recognition Letters vol 23 no 1ndash3 pp 319ndash327 2002

[16] D Han and B Lee ldquoDevelopment of early tunnel fire detectionalgorithm using the image processingrdquo in Advances in VisualComputing pp 39ndash48 Springer Berlin Germany 2006

[17] Y Chunyu F Jun W Jinjun and Z Yongming ldquoVideo firesmoke detection using motion and color featuresrdquo Fire Technol-ogy vol 46 no 3 pp 651ndash663 2010

[18] F Yuan ldquoVideo-based smoke detection with histogram se-quence of LBP and LBPV pyramidsrdquo Fire Safety Journal vol 46no 3 pp 132ndash139 2011

[19] F Lafarge X Descombes and J Zerubia ldquoTextural kernel forSVM classification in remote sensing application to forest firedetection andUrban area extractionrdquo in Proceedings of the IEEEInternational Conference on Image Processing (ICIP rsquo05) pp1096ndash1099 September 2005

[20] F Amon and A Ducharme ldquoImage frequency analysis for test-ing of fire service thermal imaging camerasrdquo Fire Technologyvol 45 no 3 pp 313ndash322 2009

[21] F Amon V Benetis J Kim and A Hamins ldquoDevelopmentof a performance evaluation facility for fire fighting thermalimagersrdquo in Defense and Security pp 244ndash252 2004

[22] F D Maxwell ldquoA portable IR system for observing fire thrusmokerdquo Fire Technology vol 7 no 4 pp 321ndash331 1971

[23] A Barducci D Guzzi P Marcoionni and I Pippi ldquoInfrareddetection of active fires and burnt areas theory and observa-tionsrdquo Infrared Physics amp Technology vol 43 no 3ndash5 pp 119ndash125 2002

[24] C Wang and S Qin ldquoAdaptive detection method of infraredsmall target based on target-background separation via robustprincipal component analysisrdquo Infrared Physics amp Technologyvol 69 pp 123ndash135 2015


[25] N Aggarwal and R K Agrawal ldquoFirst and second order sta-tistics features for classification of magnetic resonance brainimagesrdquo Journal of Signal and Information Processing vol 3 no2 pp 146ndash153 2012

[26] B Ko K-H Cheong and J-Y Nam ldquoEarly fire detectionalgorithm based on irregular patterns of flames and hierarchicalBayesian Networksrdquo Fire Safety Journal vol 45 no 4 pp 262ndash270 2010

[27] H Maruta Y Kato A Nakamura and F Kurokawa ldquoSmokedetection in open areas using its texture features and time seriespropertiesrdquo in Proceedings of the IEEE International Symposiumon Industrial Electronics (ISIE rsquo09) pp 1904ndash1908 IEEE SeoulSouth Korea July 2009

[28] C M Bautista C A Dy M I Manalac R A Orbe and MCordel ldquoConvolutional neural network for vehicle detection inlow resolution traffic videosrdquo in Proceedings of the IEEE Region10 Symposium (TENSYMP ) pp 277ndash281 IEEE Bali IndonesiaMay 2016

[29] H Wang Y Cai X Chen and L Chen ldquoNight-time vehiclesensing in far infrared image with deep learningrdquo Journal ofSensors vol 2016 Article ID 3403451 8 pages 2016

[30] C Shen Z Bai H Cao et al ldquoOptical flow sensorINSmagne-tometer integrated navigation system for MAV in GPS-deniedenvironmentrdquo Journal of Sensors vol 2016 Article ID 610580310 pages 2016

[31] A Bruhn J Weickert and C Schnorr ldquoLucasKanade meetsHornSchunck combining local and global optic flow meth-odsrdquo International Journal of Computer Vision vol 61 no 3 pp1ndash21 2005

[32] A S N Huda and S Taib ldquoSuitable features selection formonitoring thermal condition of electrical equipment usinginfrared thermographyrdquo Infrared Physics andTechnology vol 61pp 184ndash191 2013

[33] R M Haralick K Shanmugam and I H Dinstein ldquoTexturalfeatures for image classificationrdquo IEEE Transactions on SystemsMan and Cybernetics vol 3 no 6 pp 610ndash621 1973

[34] N Otsu ldquoA threshold selection method from gray-level his-togramsrdquo Automatica vol 11 pp 23ndash27 1975

[35] P Harrington Machine Learning in Action Manning Publica-tions 2012

[36] D J Hand HMannila and P Smyth Principles of DataMiningMIT Press 2001

[37] D Lin X Xu and F Pu ldquoBayesian information criterionbased feature filtering for the fusion of multiple features inhigh-spatial-resolution satellite scene classificationrdquo Journal ofSensors vol 2015 Article ID 142612 10 pages 2015

[38] F Van Der Heijden R Duin D De Ridder and D M TaxClassification Parameter Estimation and State Estimation AnEngineering Approach Using MATLAB John Wiley amp Sons2005

[39] R Kohavi ldquoA study of cross-validation and bootstrap foraccuracy estimation and model selectionrdquo in Proceedings ofthe 14th International Joint Conference on Artificial Intelligence(IJCAI rsquo95) Montreal Canada August 1995

[40] K Deb Multi-Objective Optimization Using Evolutionary Algo-rithms vol 16 John Wiley amp Sons New York NY USA 2001

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



Table 1 Conventional and vision-based features

Type Feature Advantages Disadvantages

Conventional features[6 7]

TemperatureIonizationUV light

(i) Detect presence of fireand smoke [8]

(i) Long response time [8](ii) Unable to providesufficient data for firelocating

Model-basedfeatures

Fourier transform [20]Wavelet transform [9]

(i) Frequency contentanalysis(ii) Flexible analysis of bothspace and frequency [25]

(i) Unable to be spatiallylocalized [25]

Vision-based features

Color (RGB) [9ndash12 26] (i) Fire (red)(ii) Smoke (gray)

(i) RGB camera cannotfunction in smoke-filledenvironments [2 14]

Dynamics [13 14] (motionshape change etc)

(i) Flickering flamesrecognition(ii) Smoke flow detection

(i) Can be influenced bydynamical robot motion(ii) Expensive computationfor motion compensation

Texture [12 18 19 27]

(i) Spatial characteristicsfor pattern recognition(ii) Less influenced byrotation and motion [18]

(i) The higher the ordertexture features the morethe computation

Feature maps [28] (CNNdeep learning)

(i) Superior performance inpattern recognition [29](ii) Once trainedapplicable in real-time

(i) Slow learning speed(ii) GPUs required due toexpensive computation

to aid in firefighting tasks within smoke-filled environments[20ndash22] as well as fire-front and burned-area recognition inremote sensing [23] are used in this research Due to the factthat TICs absorb infrared radiation in the long-wavelengthIR (7ndash14 microns) they are able to image surfaces even indense smoke and zero visibility environments [2 14] Inaddition TIC can provide proper information under localor global darkness for example shadows or darkness causedby damaged lighting Recently thermal images from TIC arestudied to recognize pattern and motion remotely [24] Thecameras will detect hot objects as well as thermal reflectionsoff of surfaces As a result image processing on detectedobjects must be sufficiently robust to discern between desiredobjects and their thermal reflections

This study ultimately leads the shipboard autonomousfirefighting robot (SAFFiR) whose prototype is displayed inFigure 1 to autonomously navigate toward fire outside FOVin indoor fire environments For this the robot needs toidentify clues such as smoke and smoke andfire-reflections byitself to correctly navigate toward the fireHowever the recog-nition of key features has not been fully studied This paperanalyzes appropriate combination of features to accuratelyclassify fire smoke their thermal reflections and other hotobjects using thermal infrared images Large-scale fire testswere conducted to create actual fire environments havingvarious ranges of both temperature and smoke conditionsA long-wavelength IR camera was installed to produce 14-bit thermal images of the fire environment These imageswere used to extract motion and statistical texture featuresin regions of interest (ROI) Bayesian classification wasperformed to probabilistically identify multiple classes inreal-time To identify the best combination of features for

Figure 1 A prototype of the shipboard autonomous firefightingrobot (SAFFiR) Note that the data used in this paper were notacquired from this platform

accurate classification the multiobjective optimization wasimplemented using two objective functions resubstitutionand cross-validation errors

3 Motion and Texture Features

In pattern recognition system the choice of features playsan important role in the performance of classification Both


(a) RGB images

(b) Thermal images







MNI = 1

119873119875

119873119875

sum

119894119895=1

119868119894119895

VAR = 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

STD (120590) = ( 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

)

12

SKE = 1

1205903

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

3

KUR = 1

1205904

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

4

(1)

where 119868119894119895




119862119894119895=

119875119894119895

sum119873119866

119894119895=1

119875119894119895

(2)








DIS = sum119862119894119895

1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

ENT = minussum119862119894119895log119862119894119895


CON = sum119862119894119895(119894 minus 119895)

2

INV = sum119862119894119895

1 +1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

COR = sum(119894119895 + 120583

119894120583119895minus 120583119894minus 120583119895) 119862119894119895

120590119894120590119895

UNI = sum1198622119894119895

IDM = sum

119862119894119895

1 + (119894 minus 119895)2

(3)






119902 (motion and






119896

119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902)

=

119896

119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

sum119862ℎ

119896119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

(4)






119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ) =

119902

prod

119894=1

119891 (119865119894| 119862ℎ) (5)


119873(120583119865119894|119862ℎ

Σ119865119894|119862ℎ


119891 (119865119894| 119862ℎ) =

1

radic2120587Σ119865119894|119862ℎ

119890minus(12)(119865119894minus120583119865119894|119862ℎ

)

119879Σ

minus1

119865119894|119862ℎ(119865119894minus120583119865119894|119862ℎ

)

(6)

where

120583119865119894|119862ℎ

= 120583119865119894+ Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

(119862ℎminus 120583119862ℎ)

Σ119865119894|119862ℎ

= Σ119865119894119865119894minus Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

Σ119862ℎ119865119894

(7)



(119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902))


(8)





VAR 47578119864 + 05 44154119864 + 05 44012119864 + 04 41227119864 + 04 10501119864 + 07 22343119864 + 06 13300119864 + 06 88178119864 + 05

NOP 10733119864 + 03 42287119864 + 03 32220119864 + 01 13676119864 + 02 22174119864 + 02 17105119864 + 03 59575119864 + 01 23911119864 + 02

STD 62146119864 + 02 29931119864 + 02 18314119864 + 02 10236119864 + 02 32170119864 + 03 38930119864 + 02 10534119864 + 03 46992119864 + 02


KUR 30045119864 + 00 20213119864 + 00 36553119864 + 00 16131119864 + 00 22283119864 + 00 11517119864 + 00 34848119864 + 00 11912119864 + 00

OFVN 28832119864 + 04 14156119864 + 04 11848119864 + 03 87345119864 + 02 11257119864 + 04 13722119864 + 04 29978119864 + 03 22034119864 + 03

OFVMM 86830119864 + 01 56259119864 + 01 11687119864 + 02 79444119864 + 01 17598119864 + 02 29308119864 + 02 12641119864 + 02 67234119864 + 01

















0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y


0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR


0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3




119873total =119898

sum

119911=2

(

119898

119911

) (9)






Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) RGB images

(b) Thermal images







MNI = 1

119873119875

119873119875

sum

119894119895=1

119868119894119895

VAR = 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

STD (120590) = ( 1

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

2

)

12

SKE = 1

1205903

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

3

KUR = 1

1205904

119873119875

119873119875

sum

119894119895=1

(119868119894119895minus 120583)

4

(1)

where 119868119894119895




119862119894119895=

119875119894119895

sum119873119866

119894119895=1

119875119894119895

(2)








DIS = sum119862119894119895

1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

ENT = minussum119862119894119895log119862119894119895


CON = sum119862119894119895(119894 minus 119895)

2

INV = sum119862119894119895

1 +1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

COR = sum(119894119895 + 120583

119894120583119895minus 120583119894minus 120583119895) 119862119894119895

120590119894120590119895

UNI = sum1198622119894119895

IDM = sum

119862119894119895

1 + (119894 minus 119895)2

(3)






119902 (motion and






119896

119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902)

=

119896

119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

sum119862ℎ

119896119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

(4)






119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ) =

119902

prod

119894=1

119891 (119865119894| 119862ℎ) (5)


119873(120583119865119894|119862ℎ

Σ119865119894|119862ℎ


119891 (119865119894| 119862ℎ) =

1

radic2120587Σ119865119894|119862ℎ

119890minus(12)(119865119894minus120583119865119894|119862ℎ

)

119879Σ

minus1

119865119894|119862ℎ(119865119894minus120583119865119894|119862ℎ

)

(6)

where

120583119865119894|119862ℎ

= 120583119865119894+ Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

(119862ℎminus 120583119862ℎ)

Σ119865119894|119862ℎ

= Σ119865119894119865119894minus Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

Σ119862ℎ119865119894

(7)



(119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902))


(8)





VAR 47578119864 + 05 44154119864 + 05 44012119864 + 04 41227119864 + 04 10501119864 + 07 22343119864 + 06 13300119864 + 06 88178119864 + 05

NOP 10733119864 + 03 42287119864 + 03 32220119864 + 01 13676119864 + 02 22174119864 + 02 17105119864 + 03 59575119864 + 01 23911119864 + 02

STD 62146119864 + 02 29931119864 + 02 18314119864 + 02 10236119864 + 02 32170119864 + 03 38930119864 + 02 10534119864 + 03 46992119864 + 02


KUR 30045119864 + 00 20213119864 + 00 36553119864 + 00 16131119864 + 00 22283119864 + 00 11517119864 + 00 34848119864 + 00 11912119864 + 00

OFVN 28832119864 + 04 14156119864 + 04 11848119864 + 03 87345119864 + 02 11257119864 + 04 13722119864 + 04 29978119864 + 03 22034119864 + 03

OFVMM 86830119864 + 01 56259119864 + 01 11687119864 + 02 79444119864 + 01 17598119864 + 02 29308119864 + 02 12641119864 + 02 67234119864 + 01

















0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y


0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR


0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3




119873total =119898

sum

119911=2

(

119898

119911

) (9)






Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










CON = sum119862119894119895(119894 minus 119895)

2

INV = sum119862119894119895

1 +1003816100381610038161003816119894 minus 119895

1003816100381610038161003816

COR = sum(119894119895 + 120583

119894120583119895minus 120583119894minus 120583119895) 119862119894119895

120590119894120590119895

UNI = sum1198622119894119895

IDM = sum

119862119894119895

1 + (119894 minus 119895)2

(3)






119902 (motion and






119896

119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902)

=

119896

119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

sum119862ℎ

119896119901 (119862ℎ)119896

119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ)

(4)






119891 (11986511198652sdot sdot sdot 119865119902| 119862ℎ) =

119902

prod

119894=1

119891 (119865119894| 119862ℎ) (5)


119873(120583119865119894|119862ℎ

Σ119865119894|119862ℎ


119891 (119865119894| 119862ℎ) =

1

radic2120587Σ119865119894|119862ℎ

119890minus(12)(119865119894minus120583119865119894|119862ℎ

)

119879Σ

minus1

119865119894|119862ℎ(119865119894minus120583119865119894|119862ℎ

)

(6)

where

120583119865119894|119862ℎ

= 120583119865119894+ Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

(119862ℎminus 120583119862ℎ)

Σ119865119894|119862ℎ

= Σ119865119894119865119894minus Σ119879

119862ℎ119865119894

Σminus1

119862ℎ119862ℎ

Σ119862ℎ119865119894

(7)



(119901 (119862ℎ| 11986511198652sdot sdot sdot 119865119902))


(8)





VAR 47578119864 + 05 44154119864 + 05 44012119864 + 04 41227119864 + 04 10501119864 + 07 22343119864 + 06 13300119864 + 06 88178119864 + 05

NOP 10733119864 + 03 42287119864 + 03 32220119864 + 01 13676119864 + 02 22174119864 + 02 17105119864 + 03 59575119864 + 01 23911119864 + 02

STD 62146119864 + 02 29931119864 + 02 18314119864 + 02 10236119864 + 02 32170119864 + 03 38930119864 + 02 10534119864 + 03 46992119864 + 02


KUR 30045119864 + 00 20213119864 + 00 36553119864 + 00 16131119864 + 00 22283119864 + 00 11517119864 + 00 34848119864 + 00 11912119864 + 00

OFVN 28832119864 + 04 14156119864 + 04 11848119864 + 03 87345119864 + 02 11257119864 + 04 13722119864 + 04 29978119864 + 03 22034119864 + 03

OFVMM 86830119864 + 01 56259119864 + 01 11687119864 + 02 79444119864 + 01 17598119864 + 02 29308119864 + 02 12641119864 + 02 67234119864 + 01

















0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y


0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR


0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3




119873total =119898

sum

119911=2

(

119898

119911

) (9)






Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













VAR 47578119864 + 05 44154119864 + 05 44012119864 + 04 41227119864 + 04 10501119864 + 07 22343119864 + 06 13300119864 + 06 88178119864 + 05

NOP 10733119864 + 03 42287119864 + 03 32220119864 + 01 13676119864 + 02 22174119864 + 02 17105119864 + 03 59575119864 + 01 23911119864 + 02

STD 62146119864 + 02 29931119864 + 02 18314119864 + 02 10236119864 + 02 32170119864 + 03 38930119864 + 02 10534119864 + 03 46992119864 + 02


KUR 30045119864 + 00 20213119864 + 00 36553119864 + 00 16131119864 + 00 22283119864 + 00 11517119864 + 00 34848119864 + 00 11912119864 + 00

OFVN 28832119864 + 04 14156119864 + 04 11848119864 + 03 87345119864 + 02 11257119864 + 04 13722119864 + 04 29978119864 + 03 22034119864 + 03

OFVMM 86830119864 + 01 56259119864 + 01 11687119864 + 02 79444119864 + 01 17598119864 + 02 29308119864 + 02 12641119864 + 02 67234119864 + 01

















0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y


0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR


0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3




119873total =119898

sum

119911=2

(

119898

119911

) (9)






Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 5 10VAR

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

times10minus4

times10minus4

times104

times10minus7

times106

times10minus3

2468

1012

Prob

abili

ty d

ensit

y


0

5

10

Prob

abili

ty d

ensit

y

0

05

1

Prob

abili

ty d

ensit

y

5000 100000OFVN

0

20

40

Prob

abili

ty d

ensit

y

01 02 03 040DIS

500 1000 1500 20000OFVMM

0

0005

001

Prob

abili

ty d

ensit

y0

5

Prob

abili

ty d

ensit

y

2 4 60NOP

50100150200250

Prob

abili

ty d

ensit

y

09 095 1085IND

1 15 2 2505CON

2

4

6

Prob

abili

ty d

ensit

y

02 04 06 08 10ENT

0

5

Prob

abili

ty d

ensit

y

2468

1012

Prob

abili

ty d

ensit

y

06 08 104COR


0

05

1

Prob

abili

ty d

ensit

y

01

02

03

Prob

abili

ty d

ensit

y

5 10 15 200KUT

1000 2000 3000 40000STD

0

2

4

08 10604IDM

510152025

Prob

abili

ty d

ensit

y

02 04 06 08 10UNI

0

10

20

Prob

abili

ty d

ensit

y

SmokeS-reflection

FireF-reflection

SmokeS-reflection

FireF-reflection

times10minus3




119873total =119898

sum

119911=2

(

119898

119911

) (9)






Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Behavioral region



65

66

67

68

69

7

71

72

73

Cros

s-va

lidat

ion

erro

r (

)




1000800

00900

1000200

0000

800900

100200

900200

400

MNIVARNOPSTDSKE

KUROFVN

OFMMDIS

ENTCONINDCORUNIIDM








(10)














F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation





















F-measureAccuracy

90919293949596979899

Perfo

rman

ce (

)








9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










9279

9741

9504

9055

954

938

9564

9761

9662

9345

668

670

Precision

Sensitivity

F-measure

Accuracy




2000 4000 6000 8000 10000000()



IR im

age

class

ifica

tion

resu

lts

Visib

le im

age

IR im

age

imag

e ana

lysis





050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

2

0

19

4820

64




0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1462

0

0

0

1

1462

0

0

0

1

1457

0

0

0

1

1457

0

0

1365

193

197

0

0

1343

181

196

0

0

1325

205

181

0

0

1270

207

181

0

0

53

167

1929

0

0

145

166

1943

0

0

101

165

1942

7

0

49

4844

62

2

0

30

4817

63

0

0

53

165

1929

7

0

27

4830

61

MNI DIS COR SKE STD



Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bjPredicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class


140

159

0

0

0

0

0

00

0

0

0

0

0

1981

488

1280

193

144

1

1462

4838

62

25

2

MNI VAR ENT COR SKE

Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass


0

0

0

0

488

0

0

0

1

14620

0

1339

206

185

0

0

93

161

1935

2

0

13

4823

67


Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

F-re

flect

ion

Oth

er o

bj

Smok

e

Fire

S-re

flect

ion

Predicted class

Figure 9 Continued




89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












89

133

0

0

0

0

0

02

0

0

0

0

0

1888

488

0

0

0

0

488

1341

200

235

1

1459

4857

62

15

5

0

0

0

0

488

0

0

0

0

488

0

0

0

1

1458

0

0

0

1

1462

0

0

0

1

1462

0

0

1324

204

181

0

0

1334

208

181

0

0

1334

205

204

0

0

99

174

1943

0

0

91

150

1921

2

0

18

4810

65

6

0

20

4835

62

0

0

93

172

1941

2

0

22

4812

63



Smok

e

S-re

flect

ion

Fire

F-re

flect

ion

Oth

er o

bj

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflectionAc

tual

clas

s

Smok

e

F-re

flect

ion

Fire

S-re

flect

ion

Oth

er o

bj

Predicted class

Smok

e

Fire

S-re

flect

ion

Oth

er o

bj

F-re

flect

ion

Predicted class

Other obj

F-reflection

Fire

Smoke

S-reflection

Actu

al cl

ass



050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500

050010001500200025003000350040004500



6 Conclusion






Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











Appendix

See Figure 9

Competing Interests


Acknowledgments


References








2

H2

O2






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation




























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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