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Recognition of personnel fatigue state and unsafe behavior based on computer vision
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Hua LI, Lizhou WU, Xingrun ZHONG, Liangwei GUO, Yuxin CUI
China Safety Science Journal | 2025, 35(3) : 28 - 35
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China Safety Science Journal | 2025, 35(3): 28-35
• Safety social science and safety management •
Recognition of personnel fatigue state and unsafe behavior based on computer vision
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Hua LI, Lizhou WU, Xingrun ZHONG, Liangwei GUO, Yuxin CUI
Affiliations
  • School of Resources Engineering，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.0749
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Taking improving the safety and efficiency of tower crane operation as an example，a method of integrated identification of fatigue state and unsafe behavior was proposed in order to detect the potential safety hazards of drivers in real time. A live video stream was captured via a camera，and the video was analyzed and pre-processed to extract critical information for identifying subsequent fatigue and unsafe behavior. In terms of fatigue state recognition，the analysis method based on the state of eyes and mouth was used to monitor the physiological indicators such as the state of eyes opening and closing，the blink frequency and yawn frequency. In terms of unsafe behavior identification，computer vision and deep learning technology were combined to detect the potential dangerous operations of drivers in real time，thus ensuring timely detection of safety risks. The results show that the performance of the optimized YOLOv5-ECA(Efficient Channel Attention) model is significantly improved in fatigue state and unsafe behavior recognition. The accuracy rate and recall rate of the model on the test set are more than 90%，showing good recognition ability.

computer vision  /  fatigue state  /  unsafe behavior detection  /  tower crane driver  /  YOLOv5
Hua LI, Lizhou WU, Xingrun ZHONG, Liangwei GUO, Yuxin CUI. Recognition of personnel fatigue state and unsafe behavior based on computer vision[J]. China Safety Science Journal, 2025 , 35 (3) : 28 -35 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0749
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Year 2025 volume 35 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.03.0749
  • Receive Date：2024-09-20
  • Online Date：2025-07-05
  • Published：2025-03-28
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  • Received：2024-09-20
  • Revised：2024-11-23
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    School of Resources Engineering，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
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