Detection of vehicles in infrared imagery using shared weight neural network feature detectors
conference paper
In this paper, we discuss the possibility of using artificial neural networks (ANNs) as feature detectors in automatic
target recognition (ATR). The goal is to discern a vehicle in an infrared image. We train ANNs to recognize the most
easily recognizable parts of the vehicles, the wheels. The specific ANNs we use, shared weight ANNs, are especially
adept at such an image recognition task due to their specialized architecture. The feature detection stage results in
an image containing in each pixel the output of the ANN, indicating its confidence in the classification. We can then
use a simple sequence of image processing algorithms on this image to find peaks and, by counting the number of
these peaks, vehicles. This system is tested on sensitivity to scale differences and background clutter and is shown
to perform quite well.
target recognition (ATR). The goal is to discern a vehicle in an infrared image. We train ANNs to recognize the most
easily recognizable parts of the vehicles, the wheels. The specific ANNs we use, shared weight ANNs, are especially
adept at such an image recognition task due to their specialized architecture. The feature detection stage results in
an image containing in each pixel the output of the ANN, indicating its confidence in the classification. We can then
use a simple sequence of image processing algorithms on this image to find peaks and, by counting the number of
these peaks, vehicles. This system is tested on sensitivity to scale differences and background clutter and is shown
to perform quite well.
Topics
TNO Identifier
95080
Publisher
SPIE
Source title
Signal Processing, Sensor Fusion, and Target Recognition VII, 13-15 April 1998, Orlando, FL
Editor(s)
Kadar, I.
Place of publication
Bellingham, WA.
Pages
247-258
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