- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di 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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Informazioni sul capitolo
Titolo del capitolo
Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
Autori
Minsoo Park, Seungsoo Lee, Woonggyu Choi, Yuntae Jeon, Dai Quoc Tran, Seunghee Park
DOI
10.36253/979-12-215-0289-3.62
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846