Abstract: Managing the illegal use of high beams can reduce the occurrence of night traffic accidents.However,at present,there is no efficient detection method for high beam of night,and relevant traffic regulations cannot be effectively implemented.In order to solve this problem,an algorithm to detect the high beam at night is proposed in this paper.Based on YOLOv3,this algorithm optimizes the network structure of YOLOv3,accelerates its operation speed,uses standard residual components and dilates convolution to enhance the feature expression ability of the network,and then the loss function of YOLOv3 is improved to optimize the contribution of small-scale target to coordinate loss,which enhances the detection ability of small-scale target,finally YOLOv3 prior frame clustering algorithm and number are optimized to improve the expression ability and detection speed of the model.The experimental results show that the mean average precision (mAP) of the algorithm designed in this paper is 99.09%,and 30% higher than that of YOLOv3.The algorithm satisfies the practical requirement and can detect the violation effectively.

Key words: Deep learning, Dilated convolution, High beam detection, ResNet, Traffic management, YOLOv3