Evaluation of bird detection efficiency of ProBirdTracking missed detection and false positives

Hubert Lagrange & Pauline Rico

I. Introduction

Context

Reducing impact of industrial wind turbines on wildlife, particularly on raptors, is one of the challenges to carry out for achieving the environmentally friendly development of this renewable energy.

One of the possible work options is the real time detection of the threatened birds, coupled with an appropriate action (warning signal and/or wind turbine slow down). This is the aim of ProBird, and we present here a feedback compiled from 10 wind farms (42 wind turbines) in France and Germany equipped since more than one year.

I. Introduction

ProBird is an innovative tool dedicated to reduction of impact of wind turbine on Birds

I. Introduction

ProBird system

Developed since 2014 buy a biologist team with support of highly specialized

programmers in real time images analysis, network administration, industrial

communication, neural network and customers.

Main characteristics

- Panoramic horizontal view

- Extended spectrum range ( 350 -980 nm, 0,001 lux)

- 25 frames per second

- Dedicated data compression for full time year record

- Full numerical communication with wind turbine, XML OPC, COM/DCOM OPC, IEC

- Hybrid centralized / distributed computational power

- Compliance with high security VPN systems

- 3D reconstruction of scenery

- Rugged aeronautic aluminum and mineral glass for reaching the wind turbine lifetime

50 wind Turbines equipped in Belgium, Germany, and France

Main concerned species

Kites, Golden Eagle, Common Buzzard, Harriers, Kestrel, Vultures, Skylarks…

II. Material and Methods

II.1 Wind facilities

42 wind turbines used for this test

II. Material and Methods

II.1 Objectives

Evaluate cameras performances- Compare human bird detection and recorded video by human analysis - Compare 4k video vs ProBird video by human analysis of the videos- Follow drones and kite by human analysis on ProBird videos

Evaluate accuracy of detection and classification of target- Compare human field bird detection with ProBird detection- Compare radar bird detection with ProBird detection- Wrong test erone detection by ProBird- influence of weather and other parameters

Test wind turbine communication- Time to receive the order- Time to stop the wind turbine- Impact on wind turbine lifetime?

Availability of data - Full time records -> count and understand missed detection- WT parameters- Detections and warning and stop records

II. Material and methods

II.2 Evaluate cameras performances

Tools- Kites

Specifically designed kite tested in North Germany providing target from 50 cm to 200 cm of wingspan

- DronesBird Shaped, Mavic Pro, Parrot Disco, Photo drone30 cm to 110 cm. (1 wind farm in Germany, 1 wind farm in France, 1 project in France)

- RadarUse radar to have the precise position of the birds flying in the camera field of viewCombined with bird presence, allow ideal evaluation of detection distance( 3 wind farms in south of France)

- Weather and otherImpact of the weather conditions need to be detailed(rain, fog, snow, autumn leaves…)

II. Material and methods

II.3 Evaluate accuracy of detection and classification of target

Tools- Birds- Human Observers

- Birds + Human Observers + ProbIrd - > 3 wind frams in Germany and 3 wind farms in south of France

- Radar (3 wind farms in south of France)

- Weather and other factors

To be avoided : DronesDrones offer cheap, easy to handle target for testing the camera performances (optic+ Sensor+ sensitivity + Compression), however for most of them, the flight speed is above the normal flight of bird (+ significant differences in shapes, motions and trajectories).

Here we try to check the accuracy of classification between birds (our target) and false positives (planes, clouds, blades tips, insects…)…

III. Results

III.1 Evaluate cameras performances

- Drones

III. Results

III.1 Evaluate cameras performances

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Detection distance dependingon visibility level

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Detection distance depending on wingspanin optimal conditions

Drone test of detection distance and extrapolation to bird size

III. Results

III.2 Evaluate accuracyof detectionand targets classification

Birds and Human Observers(Biologists)

This algorithm is divided in 4 main steps:

1) motion detection with a quick size filter

2) blades removal based on a first basic

shape analysis

3) enhanced shape analysis to reject

clouds and vegetation motion

4) trajectory (speed, linearity, shape shift) analysis.

III. Results

III.2 Evaluate accuracy of detection and targets classification

III. Results

III.2 Evaluate accuracy of detection and targets classification

III. Results

III.2 Evaluate accuracy of detection and classificationof target

- Weather and other factors

III. Results

III.3. Test wind turbine communication

- Time to receive the order

- Time to stop the wind turbine

- Impact on wind turbine lifetime?

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Stop Order

III. Results

III.5. Data

III. Results

False positive and missed detections

The comparison between the automated detection managed by this algorithm was conducedon a subsample of 20 000 minutes of record. These 20 000 minutes of record contain 428detection of bird. For each detection± 0.024 missed detection are reported while ± 0.16 falsedetection are induced, mostly by clouds.

16,0% of false positives

2,4% of missed targets

Time to shutdowna wind turbine and detection distance

Speed [m.s-1] Speed [km.h-1] Speed [mph] Distance 35s

1 3,6 2,23 35

2 7,2 4,46 70

3 10,8 6,69 105

4 14,4 8,92 140

5 18 11,15 175

6 21,6 13,38 210

7 25,2 15,61 245

9 32,4 20,07 315

10 36 22,3 350

11 39,6 24,53 385

12 43,2 26,76 420

13 46,8 28,99 455

14 50,4 31,22 490

15 54 33,45 525

16 57,6 35,68 560

17 61,2 37,91 595

18 64,8 40,14 630

19 68,4 42,37 665

20 72 44,6 700

VI. Conclusion and perspectives

Feed back of this test

• The main characteristics of the detection of the system are validated with more than 1 year of test and feed back on the 42 test wind turbines,

• The low level of false positive allow to avoid useless production loss or habituation of the bird,

• High detection distance allow to understand the bird behavior at long range and proper detection in bad weather conditions,

• And the time between an order and the slow down of the blade are compatible with the distance of detection.

Perspectives and further work

• These results were obtained 6 months ago…

• The new version of ProBird integrate CNNs, and the first test suggest a amount of false positive below 0.8 %

Thanks for Watching!