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Traffic Flow Detection Based on YOLOv4 and Improved DeepSORT Algorithm
WANG Ji-chao, ZHANG Li-juan, ZHANG Chun-qian, HUI Zhen-qiao, Shen Yao-hui
2023, 33(1):
6-11.
DOI: 10.16046/j.cnki.issn2096-5680.2023.01.002
In order to improve the detection rate of traffic flow in different time periods, based on the excellent target detection model YOLOv4, this paper improved the traditional DeepSORT algorithm, changed the original IoU into CIoU, and provided more abundant matching strategies to make the target tracking more stable while retaining the tracking information. To a certain extent, it solves the problem that the target is easily lost when the light is dim. Finally, the improved algorithm is compared with SORT algorithm and DeepSORT algorithm in traffic flow detection stage. Experimental results show that the proposed algorithm improves by 10.7% compared with SORT algorithm and 1.3% compared with DeepSORT algorithm in daytime. At night, the proposed algorithm improves 18.1% compared with SORT algorithm and 7.9% compared with DeepSORT algorithm. In this paper, the improved algorithm is accurate in detecting traffic flow at night, which provides theoretical reference and method basis for object detection and tracking in dim environment.
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