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:

Application of neural network to Application of neural network to analyses of CCD colour TV-cameraanalyses of CCD colour TV-cameraimage for the detection of car fires in image for the detection of car fires in

expressway tunnelsexpressway tunnels

Speaker: Wu Wei-ChengSpeaker: Wu Wei-Cheng

Date: 2009/06/15Date: 2009/06/15

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OutlineOutline

1.1. IntroductionIntroduction

2.2. Simulation fire experimentSimulation fire experiment

3.3. Flow of processingFlow of processing

4.4. Extraction of flame imageExtraction of flame image

5.5. Extraction of the feature Extraction of the feature parametersparameters

6.6. Fire detection by NNFire detection by NN

7.7. ConclusionsConclusions22

1. 1. IntroductionIntroduction

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

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The simulation fire experiment of a car fire in The simulation fire experiment of a car fire in a tunnel considered the following situations:a tunnel considered the following situations:

1.1. A burning car has stopped.A burning car has stopped.

2.2. A burning car is moving.A burning car is moving.

3.3. A following car or an oncoming car is A following car or an oncoming car is approaching the burning car in situation 1.approaching the burning car in situation 1.

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2. Simulation fire experiment2. Simulation fire experiment

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2. Simulation fire experiment2. Simulation fire experiment

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3. Flow of processing3. Flow of processing

The one-frame image is compared with the The one-frame image is compared with the background image on Red intensity by background image on Red intensity by calculating its difference.calculating its difference.

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4. Extraction of flame image4. Extraction of flame image

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0Th 30Th

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Before finding feature parameters, it is Before finding feature parameters, it is necessary to standardise the image.necessary to standardise the image.

The standardisation is to expand or reduce The standardisation is to expand or reduce the zone estimated to be flame into a size the zone estimated to be flame into a size based on a standardisation distance.based on a standardisation distance.

The standardisation distance is set to 60m The standardisation distance is set to 60m and the process is performed with linear and the process is performed with linear interpolation.interpolation.

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5. Extraction of the feature parameters5. Extraction of the feature parameters

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The histogram and quartile (Q(0.25), Q(0.5), The histogram and quartile (Q(0.25), Q(0.5), Q(0.75)), and quantile (Q(0.1), Q(0.9)) of Green Q(0.75)), and quantile (Q(0.1), Q(0.9)) of Green intensity of the flame.intensity of the flame.

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The input element to NN regarding colour The input element to NN regarding colour information is the value which normalised information is the value which normalised the quartile and quantile of Red, Green, the quartile and quantile of Red, Green, and Blue intensityand Blue intensity..

The input element to NN regarding the The input element to NN regarding the area of flame is area of flame is the normalised areathe normalised area..

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If a fire output is 0.7 or more and a non-fire 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 output is 0.3 or less, it is defined as the fire and its opposite is defined as a non-fire.its opposite is defined as a non-fire.

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6. Fire detection by NN6. Fire detection by NN

The error reverse spreading method is The error reverse spreading method is used by the NN to determine the used by the NN to determine the following:following:• A flame which has changed brightness with A flame which has changed brightness with

the iris diaphragm of the camera and a ND the iris diaphragm of the camera and a ND filter.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.

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7. Conclusions7. Conclusions

A car fire which occurs less than 150m from a A car fire which occurs less than 150m from a surveillance camera is detectable by surveillance camera is detectable by standardising the distance.standardising the distance.

A car fire with flames 50 cm high is clearly A car fire with flames 50 cm high is clearly detectable using the NN which uses colour and detectable using the NN which uses colour and area information as the input elements.area information as the input elements.

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Documents

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: