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Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring

  • Seungsoo Lee
  • Seongwoo Son
  • Pa Pa Win Aung
  • Minsoo Park
  • Seunghee Park

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,
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Seungsoo Lee

Sungkyunkwan University, Korea (the Republic of)

Seongwoo Son

Sungkyunkwan University, Korea (the Republic of)

Pa Pa Win Aung

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2868-6457

Minsoo Park

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

Seunghee Park

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

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  • Anno di pubblicazione: 2023
  • Pagine: 641-647

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  • Anno di pubblicazione: 2023

Informazioni sul capitolo

Titolo del capitolo

Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring

Autori

Seungsoo Lee, Seongwoo Son, Pa Pa Win Aung, Minsoo Park, Seunghee Park

DOI

10.36253/979-12-215-0289-3.63

Opera sottoposta a peer review

Anno di pubblicazione

2023

Copyright

© 2023 Author(s)

Licenza d'uso

CC BY-NC 4.0

Licenza dei metadati

CC0 1.0

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

CC BY-NC 4.0

Licenza dei metadati

CC0 1.0

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

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Proceedings e report

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2704-601X

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2704-5846

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