Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (13): 218-226.

### Highway Vehicle Object Detection Based on Improved YOLOv4 Method

WANG Yingxuan, SONG Huansheng, LIANG Haoxiang, YU Xiaoyu, YUN Xu

1. School of Information Engineering, Chang’an University, Xi’an 710064, China
• Online:2021-07-01 Published:2021-06-29

### 基于改进的YOLOv4高速公路车辆目标检测研究

1. 长安大学 信息工程学院，西安 710064

Abstract:

Aiming at the problem of vehicle object detection in highway scenes, an improved YOLOv4 network is proposed to detect vehicles in traffic scenes. Firstly, a multi-weather, multi-period, multi-scene vehicle dataset is proposed, and vehicle detection models are obtained based on the datasets. Secondly, a multi-label detection method is proposed, and a constraint relationship between multiple labels is established to obtain more complete vehicle information. Finally, an image stitching detection method is proposed, which connects multiple images through the stitching layer for vehicle detection, so as to improve the running speed of the network. Experimental results show that the diversified dataset improves the accuracy of vehicle detection, reduces the false detection and missed detection of vehicle objects, and the improved network structure greatly improves the detection speed. The above methods can provide references for vehicle detection and practical applications in highway scenes.