DOI QR코드

DOI QR Code

Context- and Shape-Aware Safety Monitoring for Construction Workers

  • Wei-Chih Chern (Dept. of Electrical and Computer Engineering, University of Dayton) ;
  • Kichang Choi (Dept. of Civil and Environmental Engineering, Yonsei Univ.) ;
  • Vijayan Asari (Dept. of Electrical and Computer Engineering, University of Dayton) ;
  • Hongjo Kim (Dept. of Civil and Environmental Engineering, Yonsei Univ.)
  • Published : 2024.07.29

Abstract

The task of vision safety monitoring in construction environments presents a formidable challenge, owing to the dynamic and heterogeneous nature of these settings. Despite the advancements in artificial intelligence, the nuanced analysis of small or tiny personal protective equipment (PPE) remains a complex endeavor. In response to this challenge, this paper introduces an innovative safety monitoring system, specifically designed to enhance the safety monitoring of working both at ground level and at elevated heights. This novel system integrates a suite of sophisticated technologies: instance segmentation, shape classification, object tracking, a visualization report, and a real-time notification module. Collectively, these components coalesce to deliver a safety monitoring solution, ensuring a higher standard of protection for construction workers. The experimental results…..

Keywords

Acknowledgement

This research was conducted by the support of the "2023 Yonsei University Future-Leading Research Initiative (No. 2023-22-0114)" and the "National R&D Project for Smart Construction Technology (No. RS-2020-KA156488)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

References

  1. Census of fatal occupational injuries summary. Technical Report USDL-22-2309, U.S. Bureau of Labor Statistics, 2022 
  2. Nipun D. Nath, Amir H. Behzadan, and Stephanie G. Paal. Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction, 112:pp. 103085, April 2020. ISSN 09265805. doi: 10.1016/j.autcon.2020.103085. 
  3. Wei-Chih Chern, Jeongho Hyeon, Tam V. Nguyen, Vijayan K. Asari, and Hongjo Kim. Context-aware safety assessment system for far-field monitoring. Automation in Construction, 149:pp. 104779, May 2023. ISSN 09265805. doi: 10.1016/j.autcon.2023.104779 
  4. JackC.P. Cheng, Peter Kok-Yiu Wong, Han Luo, Mingzhu Wang, and Pak Him Leung. Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification. Automation in Construction, 139:pp. 104312, July 2022. ISSN 09265805. doi: 10.1016/j.autcon.2022.104312 
  5. Minsoo Park, Dai Quoc Tran, Jinyeong Bak, Seunghee Park. Small and overlapping worker detection at construction sites. Automation in Construction, 151:pp. 104856, July 2023. ISSN 09265805. doi: 10.1016/j.autcon.2023.104856 
  6. Siyeon Kim, Seok Hwan Hong, Hyodong Kim, Meesung Lee, Sungjoo Hwang. Small object detection (SOD) system for comprehensive construction site safety monitoring. Automation in Construction, 156:pp. 105103, December 2023, ISSN 09265805. doi: 10.1016/j.autcon.2023.105103 
  7. Glenn Jocher, Ayush Chaurasia, and Jing Qiu. YOLO by Ultralytics, January 2024. URL https://github.com/ultralytics/ultralytics. 
  8. Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan. FastViT: A Fast Hybrid Vision Transformer Using Structural Reparameterization. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2023, pp. 5785-5795 
  9. Wei-Chih Chern, Vijayan Asari, Hongjo Kim. Hashing-Based Object Tracking for Construction Site Safety Monitoring across Different Domains. ASCE International Conference on Computing in Civil Engineering, June 2023. pp. 500-507 
  10. Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. CoRR, abs/2012.07177, 2020. doi: https://doi.org/10.48550/arXiv.2012.07177. 
  11. Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz. mixup: Beyond Empirical Risk Minimization. CoRR, 2017. http://arxiv.org/abs/1710.09412 
  12. Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang. Random Erasing Data Augmentation. CoRR, 2017. http://arxiv.org/abs/1708.04896 
  13. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bjorn Ommer. High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 10684-10695 
  14. Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Guanghui Liu, Amit Raj, Yuanzhen Li, Michael Rubinstein, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri. Lumiere: A Space-Time Diffusion Model for Video Generation. arXiv, 2024. 
  15. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. CoRR, 2021. https://arxiv.org/abs/2103.00020