2. A yolo-powered computer vision approach to helmet detection for enhancing construction site safety
DOI:
https://doi.org/10.61591/jslhu.26.725Từ khóa:
Helmet Detection Computer Vision Real-time Safety Monitoring YOLOv12 Worker Safety.Tóm tắt
Ensuring the safety of workers is of utmost importance in construction management, with helmet compliance serving as a crucial preventive measure against head injuries. This paper introduces an advanced “SmartSafety” system that employs computer vision technology, utilizing the cutting-edge YOLO (You Only Look Once) version 12 (YOLOv12) for real-time detection of helmets at construction sites. By analyzing high-resolution video footage from strategically positioned cameras, our deep learning model achieves an average mAP@0.5 accuracy exceeding 97%, effectively distinguishing individuals wearing helmets. The model's effectiveness is underscored by a consistent decrease in loss and enhancements in training metrics. Experimental results under diverse environmental conditions, including varying lighting and dynamic worker movements, further illustrate the system’s robustness. Beyond fostering compliance with safety regulations, this system encourages a proactive safety culture and opens avenues for scalable applications in occupational health management. Our findings underscore the transformative potential of computer vision technologies in enhancing safety and intelligence within construction environments.
Tài liệu tham khảo
Occupational Safety and Health Administration (OSHA), “Department of Labor encouraged by decline in worker death investigations,” Nov. 4, 2024. [Online]. Available: https://www.osha.gov/news/newsreleases/osha-national-news-release/20241104. [Accessed: Apr. 11, 2025].
Occupational Safety and Health Administration (OSHA), “OSHA announces switch from traditional hard hats to safety helmets to protect agency employees from head injuries better,” Dec. 11, 2023. [Online]. Available: https://www.osha.gov/news/newsreleases/trade/12112023. [Accessed: Apr. 11, 2025].
J. Takala, P. Hämäläinen, R. Sauni, C.-H. Nygård, D. Gagliardi, and S. Neupane, “Global-, regional- and country-level estimates of the work-related burden of diseases and accidents in 2019,” Scand. J. Work Environ. Health, vol. 50, no. 2, pp. 73–82, Mar. 2024, doi: 10.5271/sjweh.4132.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2016, pp. 21–37, doi: 10.1007/978-3-319-46448-0_2.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2015, pp. 91–99.
M. Tan, R. Pang, and Q. V. Le, “EfficientDet: Scalable and efficient object detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2020, pp. 10781–10790, doi: 10.1109/CVPR42600.2020.01079.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” Apr. 2017, arXiv:1704.04861. [Online]. Available: https://arxiv.org/abs/1704.04861.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.
A. H. M. Rubaiyat, M. T. Reza, B. E. Mneymneh, R. Khallaf, A. Ahsan, and M. ElSiragy, “Automatic detection of helmet uses for construction safety,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Workshops (WIW), Omaha, NE, USA, 2016, pp. 135–142, doi: 10.1109/WIW.2016.045.
F. Zhou, H. Zhao, and Z. Nie, “Safety helmet detection based on YOLOv5,” in Proc. IEEE Int. Conf. Power Electron., Comput. Appl. (ICPECA), Shenyang, China, 2021, pp. 6–11, doi: 10.1109/ICPECA51329.2021.9362711.
Kisaezehra, M. U. Farooq, M. A. Bhutto, and A. K. Kazi, “Real-time safety helmet detection using YOLOv5 at construction sites,” Intell. Autom. Soft Comput., vol. 36, no. 1, pp. 911–927, 2023, doi: 10.32604/iasc.2023.031359.
C. Shan, H. Liu, and Y. Yu, “Research on improved algorithm for helmet detection based on YOLOv5,” Sci. Rep., vol. 13, no. 1, p. 18056, Oct. 2023, doi: 10.1038/s41598-023-45383-x.
H. Wang, Z. Hu, Y. Guo, Z. Yang, F. Zhou, and P. Xu, “A real-time safety helmet wearing detection approach based on CSYOLOv3,” Appl. Sci., vol. 10, no. 19, p. 6732, 2020, doi: 10.3390/app10196732.
Y. Ji, Y. Cao, X. Cheng, and Q. Zhang, “Research on the application of helmet detection based on YOLOv4,” J. Comput. Commun., vol. 10, no. 8, pp. 130–141, Aug. 2022, doi: 10.4236/jcc.2022.108009.
B. P. Athidhi and P. Smitha Vas, “YOLOv7-based model for detecting safety helmet wear on construction sites,” in Intelligent Sustainable Systems. ICoISS 2023, Lecture Notes in Networks and Systems, vol. 665, J. S. Raj, I. Perikos, and V. E. Balas, Eds. Singapore: Springer, 2023, pp. 363–374, doi: 10.1007/978-981-99-1726-6_29.
L. Sun, H. Li, and L. Wang, “HWD-YOLO: A new vision-based helmet wearing detection method,” Comput. Mater. Continua, vol. 80, no. 3, pp. 4543–4560, 2024, doi: 10.32604/cmc.2024.055115.
S. S. Maharajpet, D. Mugad, and C. Nagaraj, “Advanced safety helmet detection: Enhancing industrial site safety with AI,” in Convergence of Machine Learning and IoT for Enabling the Future of Intelligent Systems, Jul. 2024, pp. 1–10, doi: 10.48001/978-81-966500-7-0-1.
S. Zhang, S. Huang, J. Qin, X. Li, Z. Zhang, Q. Fan, and Q. Tan, “Detection of helmet use among construction workers via helmet-head region matching and state tracking,” Autom. Construct., vol. 171, p. 105987, 2025, doi: 10.1016/j.autcon.2025.105987.
M. Al Rabbani Alif, “Enhancing construction site safety: A lightweight convolutional network for effective helmet detection,” Sep. 2024, arXiv:2409.12669. [Online]. Available: https://arxiv.org/abs/2409.12669. [Accessed: Apr. 11, 2025].
A. Paszke et al., “PyTorch: An imperative style, high-performance deep learning library,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2019, pp. 8024–8035.
G. Bradski, “The OpenCV library,” Dr. Dobb’s J. Softw. Tools, vol. 25, no. 11, pp. 120–125, 2000.
Redmon, J.; Farhadi, A. YOLOv3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 8–14 December 2019; p. 32.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 618–626, doi: 10.1109/ICCV.2017.74.
