- 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 for Identifying Potential Fall Hazards of Construction Worker
- Minsoo Park
- Seungsoo Lee
- Woonggyu Choi
- Yuntae Jeon
- Dai Quoc Tran
- Seunghee Park
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.62
Fall from height (FFH) is one of the major causes of injury and fatalities in construction industry. Deep learning-based computer vision for safety monitoring has gained attention due to its relatively lower initial cost compared to traditional sensing technologies. However, a single detection model that has been used in many related studies cannot consider various contexts at the construction site. In this paper, we propose a deep learning-based pose estimation approach for identifying potential fall hazards of construction workers. This approach can relatively increase the accuracy of estimating the distance between the worker and the fall hazard area compared to the existing methods from the experimental results. Our proposed approach can improve the robustness of worker location estimation compared to existing methods in complex construction site environments with obstacles that can obstruct the worker's position. Also, it is possible to provide information on whether a worker is aware of a potential fall risk area. Our approach can contribute to preventing FFH by providing access information to fall risk areas such as construction site openings and inducing workers to recognize the risk area even in Inattentional blindness (IB) situations
- Keywords:
- deep learning,
- keypoint detection,
- pose estimation,
- computer vision,
- construction site safe,
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-5096-3310
Sungkyunkwan University, Korea (the Republic of)
Sungkyunkwan University, Korea (the Republic of)
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-1777-5297
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2652-821X
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0001-8970-0668
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Chapter Information
Chapter Title
Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
Authors
Minsoo Park, Seungsoo Lee, Woonggyu Choi, Yuntae Jeon, Dai Quoc Tran, Seunghee Park
DOI
10.36253/979-12-215-0289-3.62
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