Analysis of feedforward-backpropagation neural networks used in vehicle detection

Analysis of feedforward-backpropagation neural networks used in vehicle detection

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Article ID: iaor1996231
Country: United States
Volume: 9C
Issue: 3
Start Page Number: 161
End Page Number: 174
Publication Date: Jun 1995
Journal: Transportation Research. Part C, Emerging Technologies
Authors: ,
Keywords: neural networks
Abstract:

Current vision-based vehicle detection systems use image-processing algorithms to monitor the presence of vehicles on the roads. Recent research has shown that an artificial feedforward neural network can be trained to provide similar capabilities. A properly trained and configured network should be able to recognize the presence of vehicles in the images it has never been exposed to. This paper discusses the development of a feedforward-backpropagation neural network-based vehicle detection system that recognizes and tracks vehicles with satisfactory reliability and efficiency. Various issues that are important in selecting the optimal neural network model-like the architecture of the network including the number of hidden layers, their units, learning rule, tiling characteristics of the input image and the output representation of the network-are addressed in this paper. This paper also analyzes how the neural network internally learns the mapping knowledge of the input-output training pairs. The final section describes an output post processor that produces the traditional pulse and presence signals.

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