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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录刊物：SCIE、EI

卷号：31

期号：3

页面范围：1459-1467

ISSN号：1064-1246

关键字：Vehicle detection; computer vision; ConvNet; shadow segmentation

摘要：A convolution neural network (ConvNet) based vehicle detection system is developed in view of this issue that vehicle detection based on monocular vision is susceptible to be disturbed by complex background scene. Firstly, in order to detect shadows underneath vehicles for generating the candidate regions of shadow underneath vehicle, a road detection method using edge enhancement as well as an adaptive shadow segmentation approach are applied, which are aimed to better deal with the problems of grayscale variation on road and reduce the impact of the lighting variance. Then the ConvNet's structure applied to the road traffic environment is determined and trained by the established image sample sets. The shadow regions detected wrongly as the shadows underneath vehicles are recognized by ConvNet and removed from the preliminary detection results so as to precisely verify the presence of vehicles in an image. The experimental results indicate that this algorithm described in this paper is effective and precise, which can distinguish well between vehicles and background interferences.