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Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose

Author

Listed:
  • Chengle Fang

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China)

  • Huiyu Xiang

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China)

  • Chongjie Leng

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China)

  • Jiayue Chen

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China)

  • Qian Yu

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China)

Abstract

Wearing safety harness is essential for workers when carrying out work. When posture of the workers in the workshop is complex, using real-time detection program to detect workers wearing safety harness is challenging, with a high false alarm rate. In order to solve this problem, we use object detection network YOLOv5 and human body posture estimation network OpenPose for the detection of safety harnesses. We collected video streams of workers wearing safety harnesses to create a dataset, and trained the YOLOv5 model for safety harness detection. The OpenPose algorithm was used to estimate human body posture. Firstly, the images containing different postures of workers were processed to obtain 18 skeletal key points of the human torso. Then, we analyzed the key point information and designed the judgment criterion for different postures. Finally, the real-time detection program combined the results of object detection and human body posture estimation to judge the safety harness wearing situation within the current screen and output the final detection results. The experimental results prove that the accuracy rate of the YOLOv5 model in recognizing the safety harness reaches 89%, and the detection method of this study can ensure that the detection program accurately recognizes safety harnesses, and at the same time reduces the false alarm rate of the output results, which has high application value.

Suggested Citation

  • Chengle Fang & Huiyu Xiang & Chongjie Leng & Jiayue Chen & Qian Yu, 2022. "Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5872-:d:814125
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    References listed on IDEAS

    as
    1. Chuanyang Liu & Yiquan Wu & Jingjing Liu & Jiaming Han, 2021. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images," Energies, MDPI, vol. 14(5), pages 1-19, March.
    2. Xu, Qingzhen & Huang, Guangyi & Yu, Mengjing & Guo, Yanliang, 2020. "Fall prediction based on key points of human bones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
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