Abstract: A Convolution Neural Network (CNN) based vehicle detection method was proposed. First, an edge enhancement-based road detection and an adaptive shadow segmentation approaches are put forward to resolve the grayscale variation on the road and reduce the influence of the lighting variation. Then, the CNN structure applied to the road traffic environment is determined to train the established sample sets. On the basis of this, the vehicle region, which is wrongly detected as the vehicle shadow, is recognized by CNN and removed from the preliminary detection results, thus, the final vehicle shadow is obtained. Finally, CNN is modified to three classification to verify the advantages of a strong expandability of this method. The experimental results show that this method is effective in various conditions and meets the accuracy requirement, decreasing the false positive rate.

Key words: vehicle engineering, vehicle detection, monocular vision, convolutional neural network (CNN), shadow segmentation