Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s

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Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s

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[1]TANG Lindong,YUN Lijun,LUO Ruilin,et al.Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):64-71.[doi:10. 13705 / j. issn. 1671-6833. 2024. 03. 016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 64-71 Column: Public date: 2024-04-20

Title:: Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s

Author(s):: TANG Lindong¹; 2; YUN Lijun¹; 2; LUO Ruilin³; LU Lin³; 1. College of Information, Yunnan Normal University, Kunming 650500, China; 2. Yunnan Provincial Department of Education Computer Vision and Intelligent Control Technology Engineering Research Center, Yunnan Normal University, Kunming 650500, China; 3. Yunnan Tobacco Leaf Company, Kunming 650500, China

Keywords:: automatic driving; target detection; YOLOv5s; MHSARM; CoordConv

CLC:: TP391

DOI:: 10. 13705 / j. issn. 1671-6833. 2024. 03. 016

Abstract:: A complex road traffic object detection algorithm was proposed to address the issue of traffic target detection algorithms′ inability to resist complex background interference and insufficient detection performance in the current autonomous driving scenario. At first, the multi-head self-attention residual module (MHSARM) was used to improve the feature information of the target to be inspected while decreasing the complex background interference. Secondly, in the feature fusion area, CoordConv was used instead of traditional Conv, so that the network could perceive spatial information and improve network detection accuracy. The improved YOLOv5s algorithm had stronger feature extraction ability and good generalisation ability in complex roads, and mAP_0.5 reached 93.3% and 47.4%, respectively, which was higher than that of YOLOv5s 0.9% and 1.4%. In addition, compared with the latest target detection algorithms YOLOv7 and YOLOv8, the mAP_0.5 of improved YOLOv5s improved by 1.3% and 2.2%, respectively. Compared with the latest research results of Sim-YOLOv4 algorithm on Kitti dataset, mAP_0.5 improved 2.2%.

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Last Update: 2024-04-29