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Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

  • Dai Quoc Tran
  • Yuntae Jeon
  • Seongwoo Son
  • Minsoo Park
  • Seunghee Park

The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed

  • Keywords:
  • deep learning,
  • multimodal,
  • multiCCTV,
  • synthetic data,
  • pointcloud,
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Dai Quoc Tran

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

Yuntae Jeon

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

Seongwoo Son

Sungkyunkwan University, Korea (the Republic of)

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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  6. Park, M., Tran, D. Q., Bak, J., & Park, S. (2023). Small and overlapping worker detection at construction sites. Automation in Construction, 151, 104856. DOI: 10.1016/j.autcon.2023.104856
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  • Publication Year: 2023
  • Pages: 625-633

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  • Publication Year: 2023

Chapter Information

Chapter Title

Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

Authors

Dai Quoc Tran, Yuntae Jeon, Seongwoo Son, Minsoo Park, Seunghee Park

DOI

10.36253/979-12-215-0289-3.61

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

114

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