- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring
- Seungsoo Lee
- Seongwoo Son
- Pa Pa Win Aung
- Minsoo Park
- Seunghee Park
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.63
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
- Keywords:
- deep learning,
- pose estimation,
- keypoint angle calculate,
- construction site safe monitoring,
- falls from heights,
Sungkyunkwan University, Korea (the Republic of)
Sungkyunkwan University, Korea (the Republic of)
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2868-6457
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-5096-3310
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0001-8970-0668
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Chapter Information
Chapter Title
Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring
Authors
Seungsoo Lee, Seongwoo Son, Pa Pa Win Aung, Minsoo Park, Seunghee Park
DOI
10.36253/979-12-215-0289-3.63
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Book Subtitle
Managing the Digital Transformation of Construction Industry
Editors
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/979-12-215-0289-3
eISBN (pdf)
979-12-215-0289-3
eISBN (xml)
979-12-215-0257-2
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846