- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data
- Dai Quoc Tran
- Yuntae Jeon
- Seongwoo Son
- Minsoo Park
- Seunghee Park
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.61
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,
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2652-821X
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-1777-5297
Sungkyunkwan University, Korea (the Republic of)
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-5096-3310
Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0001-8970-0668
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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
Metadata License
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
Metadata License
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