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oapen-20.500.12657-890692024-04-03T02:23:08Z Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring Lee, Seungsoo Son, Seongwoo Aung, Pa Pa Win Park, Minsoo Park, Seunghee deep learning pose estimation keypoint angle calculate construction site safe monitoring falls from heights thema EDItEUR::U Computing and Information Technology According to the Ministry of Manpower, falling and slipping accidents are one of the most common accidents in addition, falls from heights (FFH), including accidents during scaffolding work, are still a major cause of death in the construction industry. Regular safety checks are currently being carried out on construction sites, but scaffold-related accidents continue to occur. Sensing technology is being attempted in many industrial sites for safety monitoring, but there are still limitations in terms of the cost of sensors and object detection, which are limited to certain risks. Therefore, this paper proposes a deep learning-based pose estimation approach to identify the risk of falling during scaffolding work in the construction industry. Through analysis of the correlation between unstable behavior during scaffold work and the angle of keypoints of workers, the proposed approach demonstrates the ability to detect the risk of falling. The proposed approach can prevent falling accidents not only by detecting construction site workers, but also by detecting specific risky behaviors. In addition, in limited work environments other than scaffolding work, the information on unstable behavior can be provided to safety managers who may not be aware of the risk, thus contributing to preventing falling accidents 2024-04-02T15:45:33Z 2024-04-02T15:45:33Z 2023 chapter ONIX_20240402_9791221502893_38 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89069 eng Proceedings e report application/pdf n/a 9791221502893_63.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_63 Firenze University Press 10.36253/979-12-215-0289-3.63 10.36253/979-12-215-0289-3.63 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 7 Florence open access
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According to the Ministry of Manpower, falling and slipping accidents are one of the most common accidents in addition, falls from heights (FFH), including accidents during scaffolding work, are still a major cause of death in the construction industry. Regular safety checks are currently being carried out on construction sites, but scaffold-related accidents continue to occur. Sensing technology is being attempted in many industrial sites for safety monitoring, but there are still limitations in terms of the cost of sensors and object detection, which are limited to certain risks. Therefore, this paper proposes a deep learning-based pose estimation approach to identify the risk of falling during scaffolding work in the construction industry. Through analysis of the correlation between unstable behavior during scaffold work and the angle of keypoints of workers, the proposed approach demonstrates the ability to detect the risk of falling. The proposed approach can prevent falling accidents not only by detecting construction site workers, but also by detecting specific risky behaviors. In addition, in limited work environments other than scaffolding work, the information on unstable behavior can be provided to safety managers who may not be aware of the risk, thus contributing to preventing falling accidents
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