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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

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,
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Minsoo Park

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-5096-3310

Seungsoo Lee

Sungkyunkwan University, Korea (the Republic of)

Woonggyu Choi

Sungkyunkwan University, Korea (the Republic of)

Yuntae Jeon

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-1777-5297

Dai Quoc Tran

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2652-821X

Seunghee Park

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0001-8970-0668

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  • Publication Year: 2023
  • Pages: 634-640

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  • Publication Year: 2023

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

CC BY-NC 4.0

Metadata License

CC0 1.0

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

CC BY-NC 4.0

Metadata License

CC0 1.0

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

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