IEIE Transactions on Smart Processing and Computing, vol. 7, no. 6, December 2018 https://doi.org/10.5573/IEIESPC.2018.7.6.440 440

IEIE Transactions on Smart Processing and Computing

Vision-based Railway Inspection System using Multiple Object Detection and Image Registration

Jinbeum Jang1, Heegwang Kim1, Minwoo Shin1, Jonggook Park2, Joungyeon Kim2, and Joonki Paik1,*

1 Department of Imaging, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul 06974, Korea {jinbeum23, heegwang27, minwoo03d0}@gmail.com, paikj@cau.ac.kr

2 2iSYS Co., Ltd. {pjk, virtual}@2iSYS.com

* Corresponding Author: Joonki Paik, paikj@cau.ac.kr

Received December 12, 2018; Accepted December 21, 2018; Published December 30, 2018

* Regular Paper

Abstract: Image processing and computer vision techniques have been utilized for safety and maintenance in the railway field. Although a lot of research has been proposed to automatically inspect a facility, most diagnosis for facility maintenance is still dependent on a manager’s subjective judgment. This paper presents a novel railway-inspection system using object detection and image subtraction based on registration. For accurate deformation and defect inspection, the proposed system compares a pair of two high-resolution images acquired by a laser scan camera equipped on a railway vehicle. The proposed system consists of three parts: i) object detection using classifiers learned by random forest, ii) facility position alignment using phase correlation matching, and iii) deformation and defect detection using image registration and subtraction. The proposed inspection system performs automatic inspections by detecting facilities and any deformed regions. Therefore, the proposed system can provide improvement of a maintenance system at a cost reduction.

Keywords: Railway inspection, Computer vision, Image processing, Random forest, Registration 1. Introduction

Automatic analysis system based on computer vision techniques has been steadily developed and utilized in various areas. Video surveillance systems provide some information summarized by object-based scene analysis techniques. Vehicles are adopting autonomous driving systems based on various cameras, radar, and light detection and ranging (LiDAR) sensors. In industrial areas, image and video analysis systems have a role in component and facility inspections for safety and maintenance [1-4]. Cameras generate high-resolution images of tiny defects, such as cracks, object deformations, and foreign materials that the human eye cannot distinguish. Inspection systems then automatically analyze them based on image processing and computer vision techniques.

Maintenance in the railway field is most important for safety because railway vehicles depend on various facilities installed outside them, such as electrification systems to supply power, and railway tracks to travel on. For defect diagnosis, some vehicles are equipped with

cameras and laser scanners to acquire images of facilities, such as tracks and overhead conductor rails [5, 6]. Although many automatic inspection systems have been developed, most maintenance systems are dependent on subjective judgment by a manager for on-site inspection. In other words, numerous facilities are still checked by a few managers for railway maintenance during a routine inspection period. For that reason, existing maintenance systems in facilities have difficulty preventing railway accidents from occurring due to critical faults.

To automatically detect defects in facilities, there are some research based on image processing and computer vision techniques. Zhang et al. proposed an automatic detection and classification method for tunnel cracks [7]. This method acquires subway tunnel images using a line scan camera, and then detects cracks using morphological processing and an extreme learning machine. Karakose et al. presented a rail track fault–diagnosis system based on a computer vision method [8]. This method analyzes the profiles of track lines extracted by a canny edge detector. Marino et al. proposed a hexagonal bolt detection method for a real-time railway maintenance system [9]. Xiong et al.

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proposed a three-dimensional profiling system to detect rail surface defects [10]. After acquiring the profile of a rail surface model using a laser scanner, defects are detected by K-means clustering and a decision tree classifier. Wu et al. tried to inspect defects on rail surfaces using unmanned aerial vehicles (UAV) and an optimal segmentation method [11].

This paper presents a novel inspection system for railway maintenance. In particular, the proposed system aims to check facilities installed outside of railway vehicles. Since most facilities in a tunnel supply electric power to railway vehicles, some defects in parts of them cause suspension of the transportation service and even accidents.

The proposed system uses images acquired by a line scan camera equipped on a railway vehicle. Although it has only one color channel, the images have a high resolution at about 1.2 mm/pixel. In addition, we use a dataset consisting of image pairs captured at different times to detect areas of deformation between two images. The proposed system first detects various facilities in tunnels using random forest–based object detection. The classifier learned under random forest quickly detects the facilities and locates their positions from a large tunnel wall region. Next, we align the facility’s positions using phase correlation to intuitively compare a pair of images. Finally, the proposed system inspects deformations and defects in the two images using image subtraction based on image registration. Unlike extracting cracks using an image, we can detect different parts between the reference and target images.

This paper is organized as follows. Section 2 introduces the proposed facility inspection system, and then the performance of the proposed system is demonstrated in Section 3. Section 4 concludes the paper.

2. Facility Inspection System

2.1 Overview The proposed inspection system detects deformed

facilities. In this paper, we assume an inspection situation of facilities installed at the top of a tunnel for an underground ralway. Most of the facilities supply strong electric power to operate railway vehicles. To inspect the facilities efficiently, the proposed system detects deformed regions using two images of the same facility acquired on different days. As a result, defects are detected by comparing the previously acquired image, ( , )bi x y , a so-called reference image, and the target image, ( , )ai x y .

Fig. 1 shows the flowchart of the proposed inspection system. It consists of three parts: i) facility detection using a classifier learned via random forest tree, ii) the facility’s position alignment using phase correlation, and iii) deformation detection based on registration-based image subtraction.

2.2 Image acquisition To check the facilities in an underground tunnel, we

need to acquire images with a wide field of view. As shown in Fig. 2, a vehicle drives on the railroad by obtaining electric power from an overhead rigid conductor line using power collectors on all sections. Therefore, massive data storage is needed in the inspection system if we acquire images using complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD)

Fig. 1. Flowchart of the proposed system.

Fig. 2. Illustration of vehicle’s driving configuration.

(a) (b)

(c)

Fig. 3. Image acquisition process of the proposed inspection system (a) scanning manner of a laser scan camera, (b) an illustration of the top-front of a railway vehicle on which a laser scan camera is installed, (c) the acquired image data. In (c), the top image is the reference image, and the bottom is the target.

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image sensors. For efficient management of image data, we acquire

images using a laser scan camera. The laser scan camera acquires image data by scanning narrow vertical lines, as shown in Fig. 3(a). The camera is installed on the top of the vehicle, as shown in Fig. 3(b). Each line belongs to a part of the vertical lines of an image. So, we can collect one-dimensional vertical signals on the tunnel wall and generate an image with a long horizontal size, as shown in Fig. 3(c).

2.2 Facility Detection using Random Forest Learning

The proposed inspection system first applies an object detection technique for efficient facility inspection without a background. As shown in Fig. 3(c), the gathered image consists of some facilities of the same types as in large tunnel wall regions. The same objects in two images acquired of the same section have different positions because of vehicle jitter and different driving velocities. Therefore, we cannot perform simple image subtraction.

To solve the problem, we detect facilities using a classifier learned by random forest [12]. Random forest is an improved learning method for decision trees. Unlike a decision tree that learns a classifier with one hierarchical tree, random forest generates multiple trees and learns them using bootstrap aggregation [13, 14]. Random forest quickly learns the classifier without the overfitting problem, in comparison to the decision tree and the neural network. So random forests are used for various purposes [15].

Fig. 4 shows the process of classifier learning and detection. To learn a classifier for facility detection, the proposed method first extracts features. We cannot use color information as features, because input images acquired by a laser scan camera include only one channel. For that reason, the proposed system uses histogram of oriented gradients (HOG) [16] and local binary pattern (LBP) [17] to extract single-channel features. HOG features represent gradient information with orientation and magnitude, and LBP represents textures of objects. After vectorizing the two feature channels, the classifier

are learned using the random forest model. In the detection process, the proposed system extracts

features from a pair of images, and then it detects the facilities using the learned classifier. To reduce overlapping regions, we perform non-maximum suppression.

2.3 Facility Alignment based on Phase Correlation Matching

In the previous step, the proposed inspection system detects facilities from the reference and target images. Ideally, the same objects are detected from two images that have precisely the same positions, if the classifier is to provide the best performance. However, it is difficult to meet this criterion because two images are usually acquired under different driving conditions, including amounts of jitter and the vehicle’s velocity. In addition, we face two additional situations for detected objects: i) where positions are misaligned, although the proposed system detects the same facilities in the two images, and ii) where an object is detected in one of the images.

To solve these problems, the proposed system aligns the positions of the detected facilities pointing at the same object using phase correlation matching. Phase correlation is one image registration method using normalized cross correlation in the Fourier domain [18]. After two input images, ( , )bi x y and ( , )ai x y , are first transformed into their frequency versions, ( , )bI u v and ( , )aI u v , by Fourier transform, the normalized cross correlation is

*

*

( , ) ( , )( , )( , ) ( , )

b a

b a

I u v I u vR u vI u v I u v

= , (1)

where ( , )R u v represents the response of the normalized cross correlation of ( , )bI u v and ( , )aI u v , and * indicates the conjugate signal. Finally, we can obtain displacement vector ( , )x yΔ Δ from the inverse Fourier transform of

( , )R u v , as follows

( ) { }( , )

, arg max ( , )x y

x y r x yΔ Δ = , (2)

Fig. 4. Learning and detection process of the proposedsystem.

Fig. 5. Image cropping strategy for facility alignment based on phase correlation.

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where ( , )r x y denotes the inverse version of ( , )R u v . In the proposed inspection system, we apply phase

correlation by cropping two images into sub-region images. As shown in Fig. 5, we register two sub-images using phase correlation after cropping sub-images to a size of h h× from the two images. To address situations where facilities are divided into two sections, the proposed system crops images by overlapping some regions.

Next, we perform facility alignment using the displacement vector obtained from phase correlation. As mentioned above, there are two cases in which to merge positions of the detected objects as the same object in the reference and target images. When two detected objects indicate the same facility with misaligned positions, we merge regions of interest by computing the outermost pixels, as shown in Fig. 6(a). If the facility is detected in one of the two comparison images, the proposed system assigns an object position from another image using a displacement vector and the detected region, as shown in Fig. 6(b).

2.4 Deformation inspection process Given the detected regions with an aligned position, we

finally detect facility deformation between the reference and target images. Since images are acquired by a laser scan camera, all vertical lines have different driving velocities and accelerations. Fig. 7 shows the result of image subtraction between the reference and target images.

As shown in Fig. 7(c), the subtraction result detects a deformed region with other normal regions because of geometric distortion that occurred due to various factors.

The proposed system detects deformed regions using an image registration and matching approach. After extracting features using speeded-up robust features (SURF) [19] and random sample consensus (RANSAC) [20], the proposed system will estimate the homography from the extracted features. Since the homography represents the geometric relationship between two images projected onto a 2D image, we can transform an image into a shape similar to its corresponding image. Fig. 8 shows the image subtraction result between a reference image transformed by the homography and a target image. Comparing it to the results in Fig. 7(c), we can obtain an improved result, as shown in Fig. 8(b). However, unexpected regions remain because of the global transformation.

To solve the problem, we subtract two images using image translation and error measurement. First, we obtain the absolute difference between two smoothed versions of transformed reference image ( , )p

ni x y and target image ( , )a

ni x y for the n-th detected object, convolved by Gaussian function as

{ }

1 1( , ) ( , ) ( , ) ( , , )p an ne x y i x y i x y G x yσ σ= − ⊗ , (3)

where

1( , )e x yσ represents the error between ( , )p

ni x y and

( , )ani x y , 1( , , )G x y σ is the Gaussian function with scale

value 1σ , and ⊗ indicates the convolution operator. In this process, we generate two errors,

1( , )e x yσ and

2( , )e x yσ , using two Gaussian functions, with 1 2σ σ> .

Next, we translate ( , )ani x y in the horizontal direction, and

then subtract ( , )pni x y and the shifted target image

( , , )ani x y t as

{ }

1 1( , , ) ( , ,0) ( , , ) ( , , )p an ns x y t i x y i x y t G x yσ σ= − ⊗ , (4)

where

1( , , )s x y tσ denotes the subtraction results, and t is

the shifting variables in the range (-10, 10) with integer

(a) (b)

Fig. 6. Two cases for facility alignment (a) two detected objects indicate the same facility, (b) one of the two images detects the facility. The dotted line indicates the detected region in the target image, the dashed line indicates it in the reference image, and the solid line indicates the modified region.

(a) (b) (c)

Fig. 7. Result of simple image subtraction between two regions cropped in the reference and target images (a) and (b) are the facility in the reference and target images, respectively, (c) is the result of image subtraction.

(a) (b)

Fig. 8. Result of subtraction between transformed reference and target images (a) the reference image from perspective transformation using homography, (c) the results of image subtraction.

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intervals. Given the two results, 1( , )e x yσ and

1( , , )s x y tσ ,

the proposed method computes minimum error 1( , )e x yσ′ as

{ }1 1 1

( , ) min ( , ), ( , , )e x y e x y s x y tσ σ σ′ = . (5)

The proposed method repeats this process in the y-axis

and for 2( , )e x yσ . Consequently, we can obtain two

candidate images, 1

ˆ ( , )e x yσ and 2

ˆ ( , )e x yσ , via thresholding value.

Finally, the proposed method performs post-processing to detect deformed regions considered as defects;

1ˆ ( , )e x yσ

includes regions of a larger size with some overloaded parts, as shown in Fig. 9(a). On the other hand,

2ˆ ( , )e x yσ

has more detailed defect regions with an unnecessary area, as shown in Fig. 9(b). Therefore, we execute the AND operation on the two candidate images. Performing morphological opening and closing sequentially, we detect the final deformed regions as shown in Fig. 9(c).

3. Experimental Results

In this section, the performance of the proposed facility inspection system is demonstrated in some experiments.

We acquired image data using a laser scan camera equipped on a railway vehicle operating in an underground tunnel of Korea. The camera resolution is about 1.2 mm/pixel, and all images consisted of a single channel at 16,384 2,048× . In addition, all pairs of comparison images were captured from the same parts of the facility on different days.

In the first experiment, we tested the detection performance of the proposed inspection system. As shown in Fig. 10, we learned four main facilities with background regions of the tunnel. It is important to inspect the facilities since they have the role of supporting overhead rigid lines for the electric power supply. We detected the facilities in 40 images, and measured the detection accuracy.

Fig. 11 shows some detection results from the classifier learned by using random forest. The proposed system detects some main facilities despite a false positive and some undetected results.

(a) (b) (c)

Fig. 9. Results of post-processing (a) and (b) are deformation candidate images using parameters 1σand 2σ , respectively, (c) is the final inspection result.

(a) (b) (c)

(d) (e)

Fig. 10. Various facilities learned by random forest (a) an insulator, (b) to (d), each part of support fixture A, B, and C, (e) tunnel background images.

(a) 5th image

(b) 16th image

(c) 35th image

Fig. 11. Results from multiple object detection using random forest. The solid lines indicate the detection results with the proposed system, the dotted lines are undetected objects, and the ellipse is a region with a false positive.

Fig. 12. Precision-recall curve from multiple object detection using random forest.

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We measured the mean of the average precision with the precision–recall curve shown in Fig. 12. The proposed method has good performance at a high precision rate until the recall rate is about one-half of the mean average precision rate of about 74%.

Next, we experimented with the deformation inspection accuracy of the detected facilities. In this experiment, we simulated 48 deformities. Fig. 13 shows the results of the facility inspection by the proposed system. Although a mis-detected region is shown by the dotted line, the proposed method can detect deformed regions, as shown by the dashed lines in Fig. 13.

4. Conclusion

This paper presents an automatic facility inspection system based on computer vision and image processing techniques for railway maintenance. Since most facilities have the same features and shapes repeatedly installed at regular intervals, the system is effective at detecting various facilities using random forest learning. In addition, the proposed system uses image registration for facility alignment and fault detection. When neighboring frames acquired by a camera are spatially connected or stitched without distortion, image registration based on phase

correlation is a powerful method to align two images. In addition, we can obtain accurate defect information on deformed regions by simple subtraction, since homography-based transformation can provide completely registered images for reference.

As mentioned above, it is difficult to always inspect outside facilities with existing railway maintenance systems because most facilities are manually checked by only a few managers. For that reason, the proposed system can provide a new maintenance system conducting automatic facility inspection with short-period intervals to ensure safe transportation services.

Acknowledgement

This work was supported by the Ministry of Land & Infrastructure, and Transport in Korea through the Korea Railroad Technology Research “Developing autonomous inspection and analysis systems for the facilities of urban railways (tunnels)” (Project No. 18RTRP-C136506-02) commissioned by the Korea Agency for Infrastructure Technology Advancement (KAIA) and 2iSYS Co., Ltd.

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Fig. 13. Results of the facility inspection. The first column has the reference images, the second column has the target images, and the third, facility inspection results. The solid lines represent simulated defect regions, the dashed lines are detected deformed regions, and the dotted line is a mis-detected region.

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Jinbeum Jang was born in Suwon, Korea, in 1989. He received a BSc in Digital Media from Sang-Myung University, Korea, in 2014. Also, he graduated with an MSc in Image Science from Chung-Ang University, Korea, in 2016. Currently, he is pursuing a PhD in Image Science at

Chung-Ang University. His research interests include hybrid auto-focusing, camera calibration, object detection, and depth map generation.

Heegwang Kim was born in Seoul, Korea, in 1992. He received a BSc in Electronic Engineering from Soongsil University, Korea, in 2016. Also, he graduated with an MSc in Image Science from Chung-Ang University, Korea, in 2018. Currently, he is pursuing a PhD in Image Science at

Chung-Ang University. His research interests include object detection, object recognition, and video tracking.

Minwoo Shin was born in Buyeo, Korea, in 1992. He received a BSc in Electronics and Information Engi-neering from Konyang University, Korea, in 2017. Currently, he is pursuing an MSc in Image Science at Chung-Ang University. His research interests include object detection and

camera calibration.

Jonggook Park was born in Gochang, Korea in 1958. He received a BSc in Electrical Engineering from Cho-Sun University, Korea, in 1981. Also, he graduated with an MSc in Electrical Engineering from Inha University, Korea, in 1985. Currently, he is president of 2iSYS Co., Ltd.

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Joungyeon Kim was born in Ansan, Korea, in 1974. He received a BSc from the Department of Software at Se-Myung University, Korea, in 2002. Currently, he is the Director of the Institute for 2iSYS Co., Ltd.

Joonki Paik was born in Seoul, Korea, in 1960. He received a BSc in Control and Instrumentation Engineering from Seoul National University in 1984. He received an MSc and a PhD in Electrical Engineering and Computer Science from Northwestern University in 1987 and 1990, respectively. From

1990 to 1993, he worked at Samsung Electronics, where he designed image stabilization chip sets for consumer camcorders. Since 1993, he has been on the faculty at Chung-Ang University, Seoul, Korea, where he is currently a Professor in the Graduate School of Advanced Imaging Science, Multimedia, and Film. From 1999 to 2002, he was a Visiting Professor in the Department of Electrical and Computer Engineering at the University of Tennessee, Knoxville. Dr. Paik was a recipient of the Chester Sall Award from the IEEE Consumer Electronics Society, the Academic Award from the Institute of Electronic Engineers of Korea, and the Best Research Professor Award from Chung-Ang University. He has served the IEEE Consumer Electronics Society as a member of the editorial board. Since 2005, he has been the head of the National Research Laboratory in the field of image processing and intelligent systems. In 2008, he worked as a full-time technical consultant for the System LSI Division at Samsung Electronics, where he developed various computational photographic techniques including an extended depth-of-field (EDoF) system. From 2005 to 2007, he served as Dean of the Graduate School of Advanced Imaging Science, Multimedia, and Film. From 2005 to 2007, he was Director of the Seoul Future Contents Convergence (SFCC) Cluster established by the Seoul Research and Business Development (R&BD) Program. Dr. Paik is currently serving as a member of the Presidential Advisory Board for Scientific/Technical Policy of the Korean government, and is a technical consultant for the Korean Supreme Prosecutor’s Office for computational forensics.

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