A Computer Vision-Based System for Detecting Safety Helmet Compliance on Construction Sites Using YOLOv5s

Authors

DOI:

https://doi.org/10.26439/ciii2025.8657

Keywords:

Computer vision, convolutional neural networks, deep learning, object detection, safety systems

Abstract

The use of safety helmets is a critical measure for protecting construction workers; however, noncompliance remains a recurrent and high-risk issue. This paper presents a real-time computer vision system for helmet detection based on the YOLOv5s algorithm. The model was trained on more than 7 000 annotated images and deployed through a lightweight, scalable pipeline. Experimental results achieved a mean Average Precision (mAP at 0.5) of 91.9% and an optimal F1-score of 0.89 at a confidence threshold of 0.41, with an inference speed of 110 FPS. These findings demonstrate the system’s effectiveness under real-world conditions, providing accurate and fast detection suitable for on-site safety monitoring and contributing to improved compliance in construction environments.

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Author Biographies

  • Minerva A. Paz Bodero, Escuela Profesional de Ingeniería Informática, Universidad Nacional de Trujillo, Perú

    Minerva Antonella Paz Bodero is a tenth-cycle Computer Engineering student at Universidad Nacional de Trujillo. Her training and experience include UX/UI design focused on usability and prototyping in Figma, complemented by knowledge of web application development and database management. She has participated in applied research projects related to computer vision and process automation. She is co-author of the article “A Mobile Optical Mark Recognition System for Pre-University Admission Tests,” accepted for publication in Engineering Proceedings (Scopus Q3), and of the paper “A Computer Vision-Based System for Detecting Safety Helmet Compliance on Construction Sites Using YOLOv5s,” presented at the IV International Congress of Industrial Engineering of Universidad de Lima. Her areas of interest include UX/UI design, computer vision, and the development of technological solutions for applied research.

  • Piero E. Suarez Chavez, Escuela Profesional de Ingeniería Informática, Universidad Nacional de Trujillo, Perú

    Piero Enrique Suarez Chavez is a tenth-cycle Computer Engineering student at Universidad Nacional de Trujillo. He has experience developing technological solutions as a full-stack developer, participating in the construction and maintenance of web systems with an emphasis on back-end, front-end, and databases, as well as process automation using Python. He has worked on projects focused on optimizing operational workflows and implementing applications with API and data service integration. He is co-author of the article “A Mobile Optical Mark Recognition System for Pre-University Admission Tests,” accepted for publication in Engineering Proceedings (Scopus Q3), and of the paper “A Computer Vision-Based System for Detecting Safety Helmet Compliance on Construction Sites Using YOLOv5s,” presented at the IV International Congress of Industrial Engineering of Universidad de Lima. His interests focus on software development, process automation, and the application of computer vision to safety and monitoring solutions.

References

[1] U.S. Bureau of Labor Statistics, “Census of fatal occupational injuries summary, 2023,” U.S. Department of Labor, Washington, DC, USA, Rep., Dec. 2024. [Online]. Available: https://www.bls.gov/news.release/cfoi.nr0.htm

[2] Occupational Safety and Health Administration, “OSHA announces switch from traditional hard hats to safety helmets to protect agency employees from head injuries better,” U.S. Department of Labor News Release, Dec. 11, 2023. [Online]. Available: https://www.osha.gov/news/newsreleases/trade/12112023

[3] M. Bottlang, G. DiGiacomo, S. Tsai, and S. Madey, “Effect of helmet design on impact performance of industrial safety helmets,” Heliyon, vol. 8, no. 8, Art. no. e09962, Aug. 2022, doi: https://doi.org/10.1016/j.heliyon.2022.e09962

[4] A. Hayat and F. Morgado-Dias, “Deep learning-based automatic safety helmet detection system for construction safety,” Appl. Sci., vol. 12, no. 16, Art. no. 8268, Aug. 2022, doi: https://doi.org/10.3390/app12168268

[5] Y. Qian and B. Wang, “A new method for safety helmet detection based on convolutional neural network,” PLoS One, vol. 18, no. 10, Art. no. e0292970, Oct. 2023, doi: https://doi.org/10.1371/journal.pone.0292970

[6] Q. An, Y. Xu, J. Yu, M. Tang, T. Liu, and F. Xu, “Research on safety helmet detection algorithm based on improved YOLOv5s,” Sensors, vol. 23, no. 13, p. 5824, Jun. 2023, doi: https://doi.org/10.3390/s23135824

[7] 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,” Adv. Civ. Eng., vol. 2020, no. 6, pp. 1–10, Sep. 2020, doi: https://doi.org/10.1155/2020/9703560

[8] Y. Li, C. Ma, L. Li, R. Wang, Z. Liu, and Z. Sun, “Lightweight tunnel obstacle detection based on improved YOLOv5,” Sensors, vol. 24, no. 2, p. 395, Jan. 2024, doi: https://doi.org/10.3390/s24020395

[9] HelmetWearingData, “YOLOv5 hard hat detection dataset v2,” Roboflow Universe, 2023. [Online]. Available: https://universe.roboflow.com/helmetwearingdata/yolov5-hard_hat_detection/dataset/2

[10] P. Suarez and M. Paz, “Helmet Detection Project,” GitHub Repository, 2025. [Online]. Available: https://github.com/pierosuarez29/Helmet_Detection_Project

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Published

2026-06-08

Issue

No. 004 (2025): IV Congreso Internacional de Ingeniería Industrial

Section

Ponencias

License

Copyright (c) 2026 Congreso Internacional de Ingeniería Industrial de la Universidad de Lima

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This work is licensed under a Creative Commons Attribution 4.0 International License.