Affiliations:
School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China
Correspondence:
[*]
Corresponding author. Jing Lian, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China. Tel.: +86 155 2470 6235; E-mail: [email protected].
Abstract: 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.
