Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection
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Published:2019-09-08
Issue:18
Volume:9
Page:3753
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Wang Zhenzhou,Huo Wei,Yu Pingping,Qi Lin,Geng Shanshan,Cao Ning
Abstract
Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.
Funder
Science and Technology Support Plan Project of Hebei Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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1. Vehicle rear-end collision warning algorithm based on DSRC;Chen;J. Automot. Saf. Energy,2016
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