Abstract: The current research on vehicle detection is mainly about machine learning,but it is still difficult to deal with occlusion and false detection.In this paper,using deep learning methods to detect forward vehicles is more effective.This paper firstly adopts the selective search method to obtain the candidate area of the sample image,and then uses the improved FAST R-CNN training network to detect the forward vehicles on the road.The method has been tested in the KITTI vehicle public dataset.The experimental results show that the detection rate of this method is higher than that of the direct test based on CNN.The problem of occlusion and error detection is largely solved.Moreover,the widely used method extracts the circulated Harr-Like features,and then uses the Adaptive Boosting classifier algorithm.Compared in TSD-MAX traffic scene dataset,the proposed method provides a higher performance.The results show that this method improves the accuracy and robustness of vehicle detection.

Key words: Accurate rate, Convolutional neural network(CNN), Forward vehicle detection, Sample image