Real-Time Vehicle Detection Based on Improved YOLO v5

Yu Zhang, Zhongyin Guo, Jianqing Wu, Yuan Tian (), Haotian Tang and Xinming Guo ()
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Yu Zhang: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Zhongyin Guo: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Jianqing Wu: School of Qilu Transportation, Shandong University, Jinan 250061, China
Yuan Tian: School of Qilu Transportation, Shandong University, Jinan 250061, China
Haotian Tang: School of Qilu Transportation, Shandong University, Jinan 250061, China
Xinming Guo: School of Qilu Transportation, Shandong University, Jinan 250061, China

Sustainability, 2022, vol. 14, issue 19, 1-19

Abstract: To reduce the false detection rate of vehicle targets caused by occlusion, an improved method of vehicle detection in different traffic scenarios based on an improved YOLO v5 network is proposed. The proposed method uses the Flip-Mosaic algorithm to enhance the network’s perception of small targets. A multi-type vehicle target dataset collected in different scenarios was set up. The detection model was trained based on the dataset. The experimental results showed that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.

Keywords: object detection; YOLO v5; Flip-Mosaic algorithm; image processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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