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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Vehicle and Pedestrian Detection Based on Improved YOLOv5s

Journal of Vibration Testing and System Dynamics 8(2) (2024) 183--194 | DOI:10.5890/JVTSD.2024.06.003

Shun-Yong Zhou$^{1,2}$, Hao Zhu$^{1,2}$, Xue Liu$^{1,2}$, Ya-Lan Zeng$^{1,2}$, Si-Cheng Li$^{1,2}$, Yang-Ming Luo$^{1,2}$

$^1$ School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China

$^2$ Artificial Intelligence Key Laboratory of Sichuan Province, Yibin 644000, China

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An updated YOLOv5s algorithm for vehicle and pedestrian identification is proposed to address the variety of vehicle and pedestrian targets in road traffic under complex environments. First, the network's nonlinear capability is improved with the addition of the GELU activation function; Second, the hybrid pyramid pooling structure (HSPPF) is utilized in the backbone network to lessen the loss of feature layer information; Finally, transfer learning and the EIoU loss function are incorporated to enhance the model's accuracy and speed of convergence. The experimental findings demonstrate that the enhanced algorithm can reliably identify targets such as vehicles and pedestrians. Its mAP value is 94.0\%, 1.8\% faster than before the enhancement, and its detection speed is 80.6 FPS. It is more suited for complex real-world traffic circumstances and has higher detection accuracy and speed when compared to other algorithms.


The work presented in this paper was partially supported by the Program of Sichuan Provincial Department of Science and Technology (No. 2020YFSY0027) and The Innovation Fund of Postgraduate, Sichuan University of Science \& Engineering (No. Y2022129 and Y2022163), the Innovation and Entrepreneurship Program for College Students (No. S202210622033).


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