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Workers’ Unsafe Actions When Working at Heights: Detecting from Images

Author

Listed:
  • Qijun Hu

    (School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Yu Bai

    (School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Leping He

    (School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China)

  • Jie Huang

    (CECEP Construction Engineering Design Institute Limited, Chengdu 610052, China)

  • Haoyu Wang

    (College of Physics, Chongqing University, Chongqing 400044, China)

  • Guangran Cheng

    (CECEP Construction Engineering Design Institute Limited, Chengdu 610052, China)

Abstract

Working at heights causes heavy casualties among workers during construction activities. Workers’ unsafe action detection could play a vital role in strengthening the supervision of workers to avoid them falling from heights. Existing methods for managing workers’ unsafe actions commonly rely on managers’ observation, which consumes a lot of human resources and impossibly covers a whole construction site. In this research, we propose an automatic identification method for detecting workers’ unsafe actions, considering a heights working environment, based on an improved Faster Regions with CNN features (Faster R-CNN) algorithm. We designed and carried out a series of experiments involving five types of unsafe actions to examine their efficiency and accuracy. The results illustrate and verify the method’s feasibility for improving safety inspection and supervision, as well as its limitations.

Suggested Citation

  • Qijun Hu & Yu Bai & Leping He & Jie Huang & Haoyu Wang & Guangran Cheng, 2022. "Workers’ Unsafe Actions When Working at Heights: Detecting from Images," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6126-:d:818261
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    References listed on IDEAS

    as
    1. Helen Lingard, 2013. "Occupational health and safety in the construction industry," Construction Management and Economics, Taylor & Francis Journals, vol. 31(6), pages 505-514, June.
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    Cited by:

    1. Yoon-Soo Shin & Junhee Kim, 2022. "A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space," Sustainability, MDPI, vol. 14(13), pages 1-13, June.

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