1. Application of neural network to analyses of CCD colour TV-camera image for the detection of car fires in expressway tunnels Speaker: Wu Wei-Cheng Date: 2009/06/15 1

2. Outline 1. Introduction 2. Simulation fire experiment 3. Flow of processing 4. Extraction of flame image 5. Extraction of the feature parameters 6. Fire detection by NN 7. Conclusions 2

3. 1. Introduction   The detection of a car fire using a neural network (NN), which uses features of flame images in a simulation fire as input elements.   The simulation fire is photographed with a CCD colour TV camera.   Flame images are taken from the dynamic image, and features of the images for the NN application are extracted from a rectified image 3

4.  The simulation fire experiment of a car fire in a tunnel considered the following situations: 1.A burning car has stopped. 2.A burning car is moving. 3.A following car or an oncoming car is approaching the burning car in situation 1. 4 2. Simulation fire experiment

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6. 6 3. Flow of processing

7.  The one-frame image is compared with the background image on Red intensity by calculating its difference. 7 4. Extraction of flame image

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10. 10 4. Extraction of flame image

11.  Before finding feature parameters, it is necessary to standardise the image.  The standardisation is to expand or reduce the zone estimated to be flame into a size based on a standardisation distance.  The standardisation distance is set to 60m and the process is performed with linear interpolation. 11 5. Extraction of the feature parameters

12. 12 5. Extraction of the feature parameters

13.  The histogram and quartile (Q(0.25), Q(0.5), Q(0.75)), and quantile (Q(0.1), Q(0.9)) of Green intensity of the flame. 13 5. Extraction of the feature parameters

14.  The input element to NN regarding colour information is the value which normalised the quartile and quantile of Red, Green, and Blue intensity.  The input element to NN regarding the area of flame is the normalised area. 14 5. Extraction of the feature parameters

15.  If a fire output is 0.7 or more and a non-fire output is 0.3 or less, it is defined as the fire and its opposite is defined as a non-fire. 15 6. Fire detection by NN

16.  The error reverse spreading method is used by the NN to determine the following: A flame which has changed brightness with the iris diaphragm of the camera and a ND filter. A flame which has changed brightness with the iris diaphragm of the camera and a ND filter. A brake lamp. A brake lamp. A headlight. A headlight. Road surface reflection of a headlight. Road surface reflection of a headlight. A revolving emergency light. A revolving emergency light. 16 6. Fire detection by NN

17. 17 6. Fire detection by NN

18. 18 6. Fire detection by NN

19. 7. Conclusions  A car fire which occurs less than 150m from a surveillance camera is detectable by standardising the distance.  A car fire with flames 50 cm high is clearly detectable using the NN which uses colour and area information as the input elements. 19