- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di 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
- Abdelhamid, T. S., & Everett, J. G. (2000). Identifying Root Causes of Construction Accidents. Journal of Construction Engineering and Management, 126(1), 52–60. DOI: 10.1061/(ASCE)0733-9364(2000)126:1(52)
- Jeon, Y., Tran, D. Q., Park, M., & Park, S. (2023). Leveraging Future Trajectory Prediction for Multi-Camera People Tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5398–5407.
- Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems.
- Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., & Chen, K. (2022). Rtmdet: An empirical study of designing real-time object detectors. arXiv Preprint arXiv:2212.07784.
- Park, M., Tran, D. Q., Bak, J., & Park, S. (2022). Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization. International Journal of Applied Earth Observation and Geoinformation, 114.
- 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
- Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., & Koltun, V. (2020). Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 1623–1637.
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788.
- Tran, D. Q., Park, M., Jeon, Y., Bak, J., & Park, S. (2022). Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data. IEEE Access, 10, 66061–66071. DOI: 10.1109/ACCESS.2022.3184707
- Tran, D. Q., Park, M., Jung, D., & Park, S. (2020). Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System. Remote Sensing, 12(24), 4169.
- Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. DOI: 10.1109/TNNLS.2018.2876865
Informazioni sul capitolo
Titolo del capitolo
Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data
Autori
Dai Quoc Tran, Yuntae Jeon, Seongwoo Son, Minsoo Park, Seunghee Park
DOI
10.36253/979-12-215-0289-3.61
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
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
Licenza dei metadati
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846