关键词: 道路目标检测；YOLOv5；模型压缩；目标追踪

Abstract: The road target detection is one of the key technologies in the field of autonomous driving. With the development of artificial intelligence, the application of road target detection is gradually widespread. This paper proposes a new target detection method based on the YOLOv5 network. The improvements include the integration of modules in ShuffleNet V2 and the use of GhostConv to transform the traditional Conv module. First, real-time video streams are collected in different road environments, and pictures and video streams are annotated. The modules in ShuffleNet V2 are integrated into the backbone network and the GhostConv module is used to improve the Conv module, which reduces the weight of the model and has less impact on the target detection accuracy. The marked images are input to the improved YOLOv5 network for training, and the obtained model is combined with the Deep SORT algorithm for target detection and tracking. The experimental results show that the weight of the obtained results decreases a lot, while the target detection accuracy increases. The improved network is lighter and easier to deploy on edge embedded devices.

Key words: Road target detection; YOLOv5; Model compression; Target tracking