Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring
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 carr...
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| Materyal Türü: | Online |
| Dil: | İngilizce |
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Firenze University Press
2024
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| Online Erişim: | ONIX_20240402_9791221502893_38 |
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| _version_ | 1869517179480702976 |
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| author | Lee, Seungsoo Son, Seongwoo Aung, Pa Pa Win Park, Minsoo Park, Seunghee |
| author_browse | Aung, Pa Pa Win Lee, Seungsoo Park, Minsoo Park, Seunghee Son, Seongwoo |
| author_facet | Lee, Seungsoo Son, Seongwoo Aung, Pa Pa Win Park, Minsoo Park, Seunghee |
| author_sort | Lee, Seungsoo |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-137232 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1372322024-05-13T02:23:47Z 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 2024-05-13T02:23:27Z 2024-05-13T02:23:27Z 2024-04-02T15:45:33Z 2023 chapter ONIX_20240402_9791221502893_38 2704-5846 https://library.oapen.org/handle/20.500.12657/89069 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137232 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89069/1/9791221502893_63.pdf Firenze University Press 10.36253/979-12-215-0289-3.63 10.36253/979-12-215-0289-3.63 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 7 Florence open access |
| spellingShingle | deep learning pose estimation keypoint angle calculate construction site safe monitoring falls from heights thema EDItEUR::U Computing and Information Technology Lee, Seungsoo Son, Seongwoo Aung, Pa Pa Win Park, Minsoo Park, Seunghee Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title | Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title_full | Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title_fullStr | Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title_full_unstemmed | Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title_short | Chapter Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring |
| title_sort | chapter deep learning based pose estimation of scaffold fall accident safety monitoring |
| topic | deep learning pose estimation keypoint angle calculate construction site safe monitoring falls from heights thema EDItEUR::U Computing and Information Technology |
| topic_facet | deep learning pose estimation keypoint angle calculate construction site safe monitoring falls from heights thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_38 |
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