Abstract: Aiming at the problems of low occlusion target detection accuracy and poor small target detection effect in vehicle target detection, an improved target detection algorithm YOLO v4-ASC based on YOLO v4 is proposed.  By adding convolution block attention module to the tail of the backbone extraction network, the feature expression ability of the network model is improved; The loss function is improved to improve the convergence speed of the network model, and the Adam+SGDM optimization method is used to replace the original model optimization method SGDM to further improve the model detection performance. In addition, K-Means clustering algorithm is used to optimize the priori frame size, and the car, truck and bus categories in the traffic scene data set are combined as vehicle, which simplifies the problem in this paper into a two classification problem. The experimental results show that on the basis of maintaining the detection speed of the original algorithm, the proposed YOLO v4-ASC target detection algorithm achieves 70.05% AP and 71% F1-score. Compared with the original YOLO v4 algorithm, AP is improved by 9.92 percentage points  and F1 score is improved by 9 percentage points.

Key words: YOLO v4, model optimization, convolution block attention module