UAV-based automated monitoring of personal protective equipment compliance on construction sites: a field performance evaluation

Authors

DOI:

https://doi.org/10.54355/tbus/27897338.6.3.2026.0110

Keywords:

construction safety, personal protective equipment, unmanned aerial vehicle, occupational health and safety monitoring, computer vision

Abstract

Ensuring compliance with personal protective equipment (PPE) requirements remains one of the most persistent challenges in construction site safety management, and conventional manual inspection struggles to provide continuous, site-wide coverage. This study evaluates the field performance of an unmanned aerial vehicle (UAV)-based monitoring system that combines drone imagery with computer vision to automatically verify PPE compliance among construction workers. The object of the study is construction-related PPE, and the subject of the study is the effectiveness of its detection by computer vision using a UAV camera. The system developed by the authors was tested using a DJI Matrice 4E UAV (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China) at an active construction site. Based on the resulting video recordings, a dataset of more than 500 high-resolution images was compiled and manually annotated to support training and evaluation. Field performance was assessed across four scenarios monitoring four to seven PPE items simultaneously, using the percentage of matches against manually annotated reference images, descriptive statistics, and trend analysis, and the results were discussed against comparable field and benchmark studies. The average number of correctly detected objects ranged from 3.75 to 5.75 across the four scenarios, corresponding to a relative detection rate of 82.1% to 93.8% of each scenario's maximum, with a coefficient of variation not exceeding 17.8%, supporting the system's potential as a continuous, site-wide complement to manual safety inspection.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Abdulla Omarov, Department of Civil Engineering, L.N. Gumilyov Eurasian National University, Astana, Republic of Kazakhstan

PhD, Senior Lecturer

Sungat Akhazhanov, Laboratory of Applied Mechanics and Robotics, Buketov Karaganda National Research University, Karaganda, Republic of Kazakhstan

PhD, Associate Professor

Zhibek Zhamaladinova, Department of Civil Engineering, L.N. Gumilyov Eurasian National University, Astana, Republic of Kazakhstan

PhD Student

References

ILO, “Safety and Health at the Heart of the Future of Work: Building on 100 Years of Experience,” Geneva, Switzerland: International Labour Office, 2019, p. 75.

S. Akhazhanov, O. Bibalayev, R. Muratkhan, D. Mikhailov, and A. Montaeva, “Application of artificial intelligence technologies forensuringsafetyonconstructionsites,” EKTU Journal of Information and Communication Sciences, no. 3, pp. 18–31, 2026, doi: 10.51885/3134-8025_JICS_2026_1_2. DOI: https://doi.org/10.51885/3134-8025_JICS_2026_1_2

V. Patel, A. Chesmore, C. M. Legner, and S. Pandey, “Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity,” Advanced Intelligent Systems, vol. 4, no. 1, p. 2100099, 2022, doi: 10.1002/aisy.202100099. DOI: https://doi.org/10.1002/aisy.202100099

HSE, “HSG65 Managing for Health and Safety,” 3rd ed., Norwich, UK: The Stationery Office Ltd, 2013, p. 62.

M. S. Johnson, D. I. Levine, and M. W. Toffel, “Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA,” American Economic Journal: Applied Economics, vol. 15, no. 4, pp. 30–67, 2023, doi: 10.1257/app.20200659. DOI: https://doi.org/10.1257/app.20200659

K. W. Kim, “Effect of an occupational health and safety management system based on KOSHA 18001 on industrial accidents,” Work, vol. 68, no. 2, pp. 449–460, 2021, doi: 10.3233/WOR-203385. DOI: https://doi.org/10.3233/WOR-203385

A. R. Omarov, A. Zh. Zhussupbekov, O. D. Bibalaev, D. M. Mikhailov, G. K. Tanyrbergenova, and A. Garg, “Enhancing Construction Site Safety Using Computer Vision and Drones,” Eurasian Journal of Applied Engineering and Technology (EJAET), vol. 152, no. 3, pp. 201–214, 2025, doi: 10.32523/2616-7263-2025-152-3-201-214. DOI: https://doi.org/10.32523/2616-7263-2025-152-3-201-214

M. M. Alateeq, F. P. P. Rajeena, and M. A. S. Ali, “Construction Site Hazards Identification Using Deep Learning and Computer Vision,” Sustainability, vol. 15, no. 3, p. 2358, 2023, doi: 10.3390/su15032358. DOI: https://doi.org/10.3390/su15032358

I. Jeelani, K. Asadi, H. Ramshankar, K. Han, and A. Albert, “Real-time vision-based worker localization & hazard detection for construction,” Automation in Construction, vol. 121, p. 103448, 2021, doi: 10.1016/j.autcon.2020.103448. DOI: https://doi.org/10.1016/j.autcon.2020.103448

X. Zhou, S. Li, J. Liu, Z. Wu, and Y. F. Chen, “Construction Activity Analysis of Workers Based on Human Posture Estimation Information,” Engineering, vol. 33, pp. 225–236, 2024, doi: 10.1016/j.eng.2023.10.004. DOI: https://doi.org/10.1016/j.eng.2023.10.004

J. Wu, N. Cai, W. Chen, H. Wang, and G. Wang, “Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset,” Automation in Construction, vol. 106, p. 102894, 2019, doi: 10.1016/j.autcon.2019.102894. DOI: https://doi.org/10.1016/j.autcon.2019.102894

K. Kim, K. Kim, and S. Jeong, “Application of YOLO v5 and v8 for Recognition of Safety Risk Factors at Construction Sites,” Sustainability, vol. 15, no. 20, p. 15179, 2023, doi: 10.3390/su152015179. DOI: https://doi.org/10.3390/su152015179

H. Kim and T. Park, “Detection of wearing a protective gear and entering hazardous areas of worker in construction site using YOLO,” in Proceedings of the Conference on Information and Control Systems, Madurai, India: Vaigai College of Engineering, 2021, pp. 458–459.

A. Hayat and F. Morgado-Dias, “Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety,” Applied Sciences, vol. 12, no. 16, p. 8268, 2022, doi: 10.3390/app12168268. DOI: https://doi.org/10.3390/app12168268

Q. Fang et al., “Detecting non-hardhat-use by a deep learning method from far-field surveillance videos,” Automation in Construction, vol. 85, pp. 1–9, 2018, doi: 10.1016/j.autcon.2017.09.018. DOI: https://doi.org/10.1016/j.autcon.2017.09.018

Z. Zhang, Y. Tang, Y. Yang, and C. Yan, “Safety Helmet and Mask Detection at Construction Site Based on Deep Learning,” in Proceedings of the 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China: IEEE, 2023, pp. 990–995. doi: 10.1109/ICIBA56860.2023.10165396. DOI: https://doi.org/10.1109/ICIBA56860.2023.10165396

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,” Computers and Electrical Engineering, vol. 95, p. 107458, 2021, doi: 10.1016/j.compeleceng.2021.107458. DOI: https://doi.org/10.1016/j.compeleceng.2021.107458

Z. Wang, Y. Wu, L. Yang, A. Thirunavukarasu, C. Evison, and Y. Zhao, “Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches,” Sensors, vol. 21, no. 10, p. 3478, 2021, doi: 10.3390/s21103478. DOI: https://doi.org/10.3390/s21103478

L.-W. Lung and Y.-R. Wang, “Applying Deep Learning and Single Shot Detection in Construction Site Image Recognition,” Buildings, vol. 13, no. 4, p. 1074, 2023, doi: 10.3390/buildings13041074. DOI: https://doi.org/10.3390/buildings13041074

H. Son, H. Choi, H. Seong, and C. Kim, “Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks,” Automation in Construction, vol. 99, pp. 27–38, 2019, doi: 10.1016/j.autcon.2018.11.033. DOI: https://doi.org/10.1016/j.autcon.2018.11.033

H. Guo, Y. Yu, Q. Ding, and M. Skitmore, “Image-and-Skeleton-Based Parameterized Approach to Real-Time Identification of Construction Workers’ Unsafe Behaviors,” Journal of Construction Engineering and Management, vol. 144, no. 6, p. 04018042, 2018, doi: 10.1061/(ASCE)CO.1943-7862.0001497. DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001497

L. Ding, W. Fang, H. Luo, P. E. D. Love, B. Zhong, and X. Ouyang, “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory,” Automation in Construction, vol. 86, pp. 118–124, 2018, doi: 10.1016/j.autcon.2017.11.002. DOI: https://doi.org/10.1016/j.autcon.2017.11.002

M. Wang, P. K.-Y. Wong, H. Luo, S. Kumar, V.-S. K. Delhi, and J. C.-P. Cheng, “Predicting Safety Hazards Among Construction Workers and Equipment Using Computer Vision and Deep Learning Techniques,” in Proceedings of the 36th International Symposium on Automation and Robotics in Construction, Banff, AB, Canada, 2019. doi: 10.22260/ISARC2019/0054. DOI: https://doi.org/10.22260/ISARC2019/0054

R. Chen et al., “Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet,” IEEE Internet Things J., vol. 10, no. 18, pp. 15966–15979, 2023, doi: 10.1109/JIOT.2023.3268636. DOI: https://doi.org/10.1109/JIOT.2023.3268636

H. Serediuk and V. Garmash, “Neural network architecture for real-time QR code recognition,” Information Technology and Computer Engineering, vol. 21, no. 3, pp. 9–19, 2024, doi: 10.63341/vitce/3.2024.09. DOI: https://doi.org/10.63341/vitce/3.2024.09

A. M. Vukicevic, M. Petrovic, P. Milosevic, A. Peulic, K. Jovanovic, and A. Novakovic, “A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions,” Artificial Intelligence Review, vol. 57, no. 12, p. 319, 2024, doi: 10.1007/s10462-024-10978-x. DOI: https://doi.org/10.1007/s10462-024-10978-x

“SNIP 12-03-2001 Occupational safety in construction part one. General requirements,” Moscow, Russia: Russian Federation Gosstroy, 2001, p. 42.

“GOST EN 397-2012 Occupational safety standards system. Safety helmets. General technical requirements. Test methods,” Moscow, Russia: Standardinform, 2013, p. 19.

“GOST 12.4.087-84 Occupational safety standards system. Bulding. Building helmets. Specifications,” Moscow, Russia: IPK Standards Publisher, 2003, p. 6.

“ISO 20471:2013 High visibility clothing - Test methods and requirements (IDT),” Geneva, Switzerland: ISO, 2013, p. 22.

“ISO 20345:2011 Safety footwear,” Geneva, Switzerland: ISO, 2011, p. 30.

“GOST 12.4.183-91 System of safety standards. Materials for means of hands protection. Specifications,” Moscow, Russia: IPK Standards Publisher, 1991, p. 18.

R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. Da Silva, “A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit,” Electronics, vol. 10, no. 3, p. 279, 2021, doi: 10.3390/electronics10030279. DOI: https://doi.org/10.3390/electronics10030279

Downloads

Published

2026-09-30

How to Cite

Omarov, A., Akhazhanov, S., & Zhamaladinova, Z. (2026). UAV-based automated monitoring of personal protective equipment compliance on construction sites: a field performance evaluation. Technobius, 6(3), 0110. https://doi.org/10.54355/tbus/27897338.6.3.2026.0110

Issue

Section

Articles

Categories

Funding data

Most read articles by the same author(s)