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
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Outline
1.
2.
3.
4.
5.
6.
7.
Introduction
Simulation fire experiment
Flow of processing
Extraction of flame image
Extraction of the feature parameters
Fire detection by NN
Conclusions
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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
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2. Simulation fire experiment

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.
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2. Simulation fire experiment
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3. Flow of processing
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4. Extraction of flame image

The one-frame image is compared with
the background image on Red intensity by
calculating its difference.
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4. Extraction of flame image
Th  0
Th  30
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4. Extraction of flame image
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4. Extraction of flame image
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5. Extraction of the feature parameters
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

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.
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5. Extraction of the feature parameters
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5. Extraction of the feature parameters

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.
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5. Extraction of the feature parameters


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.
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6. Fire detection by NN

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.
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6. Fire detection by NN

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 brake lamp.
• A headlight.
• Road surface reflection of a headlight.
• A revolving emergency light.
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6. Fire detection by NN
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6. Fire detection by NN
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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.
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