Abstract:In order to improve the accuracy and detection speed of vehicle type recognition, an improved vehicle recognition algorithm of YOLOv5 is proposed in this paper. Firstly, the BIT-Vehicle dataset is expanded by using the monitoring video data of highway toll collection. At the same time, aiming at the imbalance of the number of pictures of each vehicle in the data set, the number of each vehicle is balanced by using image processing technologies such as image flipping, adding Gaussian noise and color gamut change, and the BIT-Vehicle-Extend dataset is constructed; Secondly, the RFB module is added to increase the network receptive field, which is helpful to capture the global features of the model; Thirdly, the parameter free SimAM attention mechanism is added to bottleneck to improve the feature extraction ability of the network without adding parameters. Finally, the experimental results show that compared with the original network model, the proposed YOLOv5 optimization algorithm, mAP0.5 and mAP0 5: 0.95 to 98.7% and 96.3%, increased by 0.7% and 1.5% respectively. In terms of detection speed, it reaches 90 frames/s, which is basically unchanged compared with the original network. Therefore, the YOLOv5 optimization algorithm proposed in this paper can detect the vehicle information in real time with high precision and meet the requirements of vehicle identification and detection.