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Predictive Safety Monitoring for Lifting Operations with Vision-Based Crane-Worker Conflict Prediction

  • Peter Kok-Yiu Wong
  • Chin Pok Lam
  • Yin Ni Lee
  • Chung Lam Ting
  • Jack C. P. Cheng
  • Pak Him Leung

Construction industry has reported among the highest accident and fatality rates over the past decade. In particular, crane lifting is a notably hazardous operation on construction sites, causing fatal accidents like workers being struck by the boom or objects fallen from tower cranes. Manual monitoring by on-site safety officers is labour-intensive and error-prone, while incorporating computer vision techniques into surveillance cameras would enable more automatic and continuous monitoring of construction site operations. However, existing studies for lifting safety mainly detect the presence of individual objects (e.g. workers, crane components), while a methodology is needed to predict their potential collision more proactively before accidents happen. This paper develops a vision-based framework for predictive lifting safety monitoring, including three modules: (1) object detection and classification: targeting at hook and lifting materials to enable danger zone estimation, along with workers and their personal protective equipment; (2) worker movement tracking and prediction: analyzing the historical moving trajectory of each unique worker to foresee his/her future movement in certain period ahead; (3) multi-level safety assessment: issuing predictive warning in real-time upon any crane-worker conflict foreseen. The proposed framework is applicable to real-time site video processing and enables end-to-end lifting safety monitoring with instant alerting upon unsafe scenarios observed. Importantly, the proposed framework predicts the future movement of workers to proactively identify potential site hazard, in order to trigger earlier safety alert for more timely decision-making. With a large video dataset capturing tower crane operations, the proposed framework demonstrates competitive accuracy and computational efficiency in crane-worker conflict prediction, validating its practicality for real-time lifting safety monitoring

  • Keywords:
  • Computer Vision; Construction Safety Monitoring; Crane-Worker Conflict Prediction; Deep Learning; Predictive Safety Assessment; Trajectory Tracking,
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Peter Kok-Yiu Wong

The Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0003-1758-675X

Chin Pok Lam

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong

Yin Ni Lee

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong

Chung Lam Ting

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong

Jack C. P. Cheng

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-1722-2617

Pak Him Leung

AutoSafe Limited, Hong Kong - ORCID: 0000-0001-8627-6216

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  • Publication Year: 2023
  • Pages: 648-656

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  • Publication Year: 2023

Chapter Information

Chapter Title

Predictive Safety Monitoring for Lifting Operations with Vision-Based Crane-Worker Conflict Prediction

Authors

Peter Kok-Yiu Wong, Chin Pok Lam, Yin Ni Lee, Chung Lam Ting, Jack C. P. Cheng, Pak Him Leung

DOI

10.36253/979-12-215-0289-3.64

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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