• 图像处理 •

### 基于YOLO v4的车辆目标检测算法

1. (长安大学信息工程学院，陕西西安710064)
• 出版日期:2022-07-25 发布日期:2022-07-25
• 作者简介:殷远齐(1997—),男,山东聊城人,硕士研究生,研究方向：计算机视觉，E-mail: 2395153341@qq.com； 徐源(1997—),男,硕士研究生,研究方向:计算机视觉,E-mail: 907799726@qq.com; 邢远新(1998—),男,硕士研究生,研究方向:计算机视觉,E-mail: 2412906899@qq.com。
• 基金资助:
国家自然科学基金资助项目（U1864204）

### Vehicle Target Detection Algorithm Based on YOLO v4

1. (School of Information Engineering， Chang’an University， Xi’an 710064， China)
• Online:2022-07-25 Published:2022-07-25

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.