Academic Journal of Computing & Information Science, 2023, 6(10); doi: 10.25236/AJCIS.2023.061005.
Yujiao Liu, Xuan Guo
College of Electronic Information, Dongguan Polytechnic, Dongguan, 523808, Guangdong, China
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.
Improved YOLOv5; Pedestrian Vehicle Detection; Recognition Algorithm
Yujiao Liu, Xuan Guo. Research on Vehicle Detection and Recognition Algorithm Based on Improved YOLOv5. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 10: 31-38. https://doi.org/10.25236/AJCIS.2023.061005.
 Li M L, Sun G B, Yu J X. A pedestrian detection network model based on improved YOLOv5[J]. Entropy, 2023, 25(2): 381.
 Xu H, Zheng W, Liu F, et al. Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5[J]. Remote Sensing, 2023, 15(14): 3583.
 Liu H, Sun F, Gu J, et al. Sf-yolov5: A lightweight small object detection algorithm based on improved feature fusion mode[J]. Sensors, 2022, 22(15): 5817.
 Sun P, Ding X. UAV image detection algorithm based on improved YOLOv5[C]//2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2022: 757-760.
 Li A, Sun S, Zhang Z, et al. A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5[J]. Electronics, 2023, 12(4): 878.
 Jia X, Tong Y, Qiao H, et al. Fast and accurate object detector for autonomous driving based on improved YOLOv5[J]. Scientific reports, 2023, 13(1): 1-13.
 Li Z, Namiki A, Suzuki S, et al. Application of low-altitude UAV remote sensing image object detection based on improved YOLOv5[J]. Applied Sciences, 2022, 12(16): 8314.
 Deng L, Bi L, Li H, et al. Lightweight aerial image object detection algorithm based on improved YOLOv5s[J]. Scientific Reports, 2023, 13(1): 7817.
 Wang H, Xu Y, He Y, et al. YOLOv5-Fog: A multiobjective visual detection algorithm for fog driving scenes based on improved YOLOv5[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-12.
 Lin Q, Zhang S, Xu S. Construction of Traffic Moving Object Detection System Based on Improved YOLOv5 Algorithm[C]//2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME). IEEE, 2023: 268-272.