Research on Vehicle Detection and Recognition Algorithm Based on Improved YOLOv5

Abstract

This paper aims to study and improve the pedestrian and vehicle detection and recognition algorithm based on YOLOv5. Firstly, the network structure of YOLOv5 is introduced, including the backbone network, neck network, and post-processing algorithm. In order to address the challenges of pedestrian and vehicle detection, this paper carefully improves the backbone network, neck network, and post-processing algorithm. Experimental results show that the improved algorithm achieves higher accuracy and better performance in pedestrian and vehicle detection tasks. By comparing the performance of different modules before and after improvement, as well as comparing with other algorithms, the superiority of the algorithm is validated. This research is of great significance for improving the application of pedestrian and vehicle detection and recognition algorithms in areas such as traffic management, intelligent monitoring, and autonomous driving, and provides useful references for related research in these fields.