Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras

Abstract

Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

Keywords

• Helmet detection
• Object detection
• YOLOv8
• Personal protective equipment.

References

1. [1] X. Huang and J. Hinze, “Analysis of construction worker fall accidents,” J. Constr. Eng. Manag., vol. 129, no. 3, pp. 262–271, 2003.
2. [2] R. A. Haslam et al., “Contributing factors in construction accidents,” Appl. Ergon., vol. 36, no. 4, pp. 401–415, 2005.
3. [3] A. Hayat and F. Morgado-Dias, “Deep learning-based automatic safety helmet detection system for construction safety,” Appl. Sci. (Basel), vol. 12, no. 16, p. 8268, 2022. https://doi.org/10.3390/app12168268
4. [4] S. Tan, G. Lu, Z. Jiang, and L. Huang, “Improved YOLOv5 network model and application in safety helmet detection,” in 2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR), 2021. https://doi.org/10.1109/ISR50024.2021.9419561
5. [5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi:10.1038/nature14539
6. [6] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv [cs.CV], 2018.
7. [7] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
8. [8] P. Doungmala and K. Klubsuwan, “Helmet wearing detection in Thailand using Haar like feature and circle Hough transform on image processing,” in 2016 IEEE International Conference on Computer and Information Technology (CIT), 2016.

9. [9] W. Zhang, C.-F. Yang, F. Jiang, X.-Z. Gao, and X. Zhang, “Safety helmet wearing detection based on image processing and deep learning,” in 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), 2020.
10. [10] S. H. Kim, C. Wang, S. D. Min, and S. H. Lee, “Safety helmet wearing management system for construction workers using three-axis accelerometer sensor,” Appl. Sci. (Basel), vol. 8, no. 12, p. 2400, 2018.
11. [11] X. Long, W. Cui, and Z. Zheng, “Safety helmet wearing detection based on deep learning,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019.
12. [12] Y. Li, H. Wei, Z. Han, J. Huang, and W. Wang, “Deep learning-based safety helmet detection in engineering management based on convolutional neural networks,” Advances in Civil Engineering, vol. 2020, pp. 1–10, 2020.
13. [13] 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. (Basel), vol. 10, no. 19, p. 6732, 2020.
14. [14] L. Huang, Q. Fu, M. He, D. Jiang, and Z. Hao, “Detection algorithm of safety helmet wearing based on deep learning,” Concurr. Comput., vol. 33, no. 13, 2021.
15. [15] G. Han, M. Zhu, X. Zhao, and H. Gao, “Method based on the cross-layer attention mechanism and multiscale perception for safety helmet-wearing detection,” Comput. Electr. Eng., vol. 95, no. 107458, p. 107458, 2021.
16. [16] K. Han and X. Zeng, "Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features," in IEEE Access, vol. 10, pp. 718-729, 2022, doi: 10.1109/ACCESS.2021.3138407.
17. [17] B. Zhang, C.-F. Sun, S.-Q. Fang, Y.-H. Zhao, and S. Su, “Workshop safety helmet wearing detection model based on SCM-YOLO,” Sensors (Basel), vol. 22, no. 17, p. 6702, 2022.
18. [18] N. D. T. Yung, W. K. Wong, F. H. Juwono, and Z. A. Sim, “Safety helmet detection using deep learning: Implementation and comparative study using YOLOv5, YOLOv6, and YOLOv7,” in 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), 2022. doi:10.1109/GECOST55694.2022.10010490
19. [19] M.-E. Otgonbold et al., “SHEL5K: An extended dataset and benchmarking for Safety HELmet detection,” Sensors (Basel), vol. 22, no. 6, p. 2315, 2022. Doi:10.3390/s22062315
20. [20] J. Chen, S. Deng, P. Wang, X. Huang, and Y. Liu, “Lightweight helmet detection algorithm using an improved YOLOv4,” Sensors (Basel), vol. 23, no. 3, p. 1256, 2023. doi:10.3390/s23031256
21. [21] Q. Zhou, J. Qin, X. Xiang, Y. Tan, and N. N. Xiong, “Algorithm of helmet wearing detection based on AT-YOLO deep mode,” Comput. Mater. Contin., vol. 69, no. 1, pp. 159–174, 2021.
22. [22] Y. Jamtsho, P. Riyamongkol, and R. Waranusast, “Real-time license plate detection for non-helmeted motorcyclist using YOLO,” ICT Express, vol. 7, no. 1, pp. 104–109, 2021.
23. [23] N. K. Anushkannan, V. R. Kumbhar, S. K. Maddila, C. S. Kolli, B. Vidhya, and R. G. Vidhya, “YOLO algorithm for helmet detection in industries for safety purpose,” in 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022.
24. [24] J. Li, Y. Li, J. F. Villaverde, X. Chen, and X. Zhang, “A safety wearing helmet detection method using deep leaning approach,” J. Opt., 2023.
25. [25] J. Fang, X. Lin, F. Zhou, Y. Tian, and M. Zhang, “Safety Helmet Detection Based on Optimized YOLOv5,” in 2023 Prognostics and Health Management Conference (PHM), IEEE, 2023, pp. 117–121.
26. [26] Z. Zhang, Y. Tang, Y. Yang, and C. Yan, “Safety Helmet and Mask Detection at Construction Site Based on Deep Learning,” in 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), vol. 3, IEEE, 2023, pp. 990–995.
27. [27] M. Gochoo, “Safety helmet wearing dataset.” Mendeley, 2021. doi: 10.17632/9rcv8mm682.1
28. [28] RangeKing, (2023), Brief Summary of YOLOv8 Model Structure, URL: https://github.com/ultralytics/ultralytics/issues/189 date of access: 01/05/2023.
29. [29] A. Kamboj and N. Powar, “Safety helmet detection in industrial environment using deep learning,” in 9th International Conference on Information Technology Convergence and Services (ITCSE 2020), 2020.
30. [30] F. Zhou, H. Zhao, and Z. Nie, “Safety helmet detection based on YOLOv5,” in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), 2021.