YOLOv10 for Real-Time Detection of Personal Protective Equipment on Construction Workers


Wawan Gunawan(1*); Yoga Nanda Khoiril Umat(2);

(1) Universitas Mercu Buana
(2) Universitas Mercu Buana
(*) Corresponding Author

  

Abstract


This study addresses the challenges of detecting Personal Protective Equipment (PPE) on construction sites, where work-related accidents frequently occur due to the incomplete use of PPE, which can lead to fatal outcomes. The objective of this research is to evaluate the use of the YOLOv10 model a lightweight and efficient object detection architecture to detect various PPE items: safety helmets, safety vests, gloves, and safety boots. The dataset consists of 1,620 images and was split using two configurations: 70:20:10 and 80:10:10 for training, validation, and testing sets, respectively. The YOLOv10 model was evaluated using the key metric of Mean Average Precision (mAP). The evaluation results demonstrate the model’s capability to accurately detect PPE, despite variations in data splitting and the number of epochs used. The findings show that the YOLOv10 algorithm performs very well in detecting PPE. On manually processed datasets, the YOLOv10-M model achieved a mAP50 of 97.8% with a 70:20:10 split and 98.4% with an 80:10:10 split. Meanwhile, on datasets processed using Roboflow, the YOLOv10-B model obtained a mAP50 of 85.2% with the 70:20:10 split, and the YOLOv10-S model reached 84.6% on the 80:10:10 split. These findings indicate that YOLOv10 delivers a significant performance improvement in PPE detection compared to previous approaches. The algorithm’s ability to achieve high mAP50 scores under certain conditions highlights its potential for real-time implementation in construction environments, where accurate and timely PPE detection is crucial to reducing future workplace accidents

Keywords


Construction; Personal Protective Equipment; PPE; YOLOv10

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 207 times
PDF view: 84 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v17i2.2544.131-139 		  

Cite

How to cite item

References


M. H. Moosa, L. P. Oriet, and A. M. Khamaj, “Measuring the Causes of Saudi Arabian Construction Accidents: Management and Concerns,” Int. J. Occup. Saf. Heal., vol. 10, no. 2, pp. 108–114, 2020, doi: 10.3126/ijosh.v10i2.33282.

M. M. Alateeq, F. R. Fathimathul, and M. A. S. Ali, “Construction Site Hazards Identification Using Deep Learning and Computer Vision,” Sustain., vol. 15, no. 3, pp. 1–19, 2023, doi: 10.3390/su15032358.

F. Arthur-Holmes and K. Abrefa Busia, “Safety concerns and occupational health hazards of women in artisanal and small-scale mining in Ghana,” Extr. Ind. Soc., vol. 10, p. 101079, 2022, doi: 10.1016/j.exis.2022.101079.

F. Ricci and M. Nucci, “Safety Integrated Model-training (SIM-t) and its evaluation: A safety training proposal for mechanical companies,” Saf. Sci., vol. 146, p. 105538, 2022, doi: 10.1016/j.ssci.2021.105538.

A. A. S. Arip, N. Sazali, K. Kadirgama, A. S. Jamaludin, F. M. Turan, and N. Ab. Razak, “Object Detection for Safety Attire Using YOLO (You Only Look Once),” J. Adv. Res. Appl. Mech., vol. 113, no. 1, pp. 37–51, 2024, doi: 10.37934/aram.113.1.3751.

I. Sutton, “Chapter 6 - Personal protective equipment,” I. B. T.-P. D. and O. Sutton, Ed., Oxford: Gulf Publishing Company, 2015, pp. 127–137. doi: 10.1016/B978-0-323-29964-0.00006-8.

M. Sharman and H. S. Mac Mahon, “Personal protective equipment,” Infect. Control Small Anim. Clin. Pract., pp. 94–111, 2023, doi: 10.1079/9781789244977.0006.

V. Isailovic, A. Peulic, M. Djapan, M. Savkovic, and A. M. Vukicevic, “The compliance of head-mounted industrial PPE by using deep learning object detectors,” Sci. Rep., vol. 12, no. 1, pp. 1–10, 2022, doi: 10.1038/s41598-022-20282-9.

H. M. Ahmad and A. Rahimi, “SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry,” pp. 1–19, 2024.

A. KORKMAZ and M. T. AĞDAŞ, “Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras,” Bitlis Eren Üniversitesi Fen Bilim. Derg., vol. 12, no. 3, pp. 773–782, 2023, doi: 10.17798/bitlisfen.1297952.

S. Dundar, A. Bilim, and N. Bilim, “Türkiye’deki yaralanmalı iş kazalarının sektörel dağılımı ve analizi,” Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg., Oct. 2023, doi: 10.28948/ngumuh.1292353.

M. Çiftçi, M. U. Türkdamar, and C. Öztürk, “Leveraging YOLO Models for Safety Equipment Detection on Construction Sites,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 492–506, 2024, doi: 10.62411/jcta.10453.

J. Bao, S. Li, G. Wang, J. Xiong, and S. Li, “Improved YOLOV8 Network and Application in Safety Helmet Detection,” J. Phys. Conf. Ser., vol. 2632, no. 1, 2023, doi: 10.1088/1742-6596/2632/1/012012.

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,” Adv. Intell. Syst., vol. 4, no. 1, p. 2100099, Jan. 2022, doi: 10.1088/1742-6596/2632/1/012012.

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.

Kemnaker, “Profil Keselamatan dan Kesehatan Kerja Nasional Indonesia Tahun 2022,” Kemnaker. Accessed: Oct. 10, 2024.

I. S. Center, “Kecelakaan Kerja di Indonesia: Data, Penyebab, dan Upaya Pencegahan,” Indonesia Safety Center. Accessed: Oct. 10, 2024.

M. Hussain, “YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision,” pp. 1–12, 2024.

M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Machines, vol. 11, no. 7, 2023, doi: 10.3390/machines11070677.

A. Wang et al., “YOLOv10: Real-Time End-to-End Object Detection,” pp. 1–18, 2024.

C.-Y. Wang and H.-Y. M. Liao, “YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems,” no. August, pp. 1–13, 2024, doi: 10.1561/116.20240058.

F. Akhmedov, R. Nasimov, and A. Abdusalomov, “Dehazing Algorithm Integration with YOLO-v10 for Ship Fire Detection,” Fire, vol. 7, no. 9, pp. 1–20, 2024, doi: 10.3390/fire7090332.

M. Mao and A. Lee, “Efficient Fabric Classification and Object Detection Using YOLOv10,” no. September, 2024, doi: 10.3390/electronics13193840.

F. Joiya, “Object Detection: Yolo Vs Faster R-Cnn,” Int. Res. J. Mod. Eng. Technol. Sci., no. 09, pp. 1911–1915, 2022, doi: 10.56726/irjmets30226.

O. ÖNAL and E. DANDIL, “Object Detection for Safe Working Environments using YOLOv4 Deep Learning Model,” Eur. J. Sci. Technol., no. 26, pp. 343–351, 2021, doi: 10.31590/ejosat.951733.

X. Ji, F. Gong, X. Yuan, and N. Wang, “A high-performance framework for personal protective equipment detection on the offshore drilling platform,” Complex Intell. Syst., vol. 9, no. 5, pp. 5637–5652, 2023, doi: 10.1007/s40747-023-01028-0.

J. A. L. Cabrejos and A. Roman-Gonzalez, “Artificial Intelligence System for Detecting the Use of Personal Protective Equipment,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 5, pp. 580–585, 2023, doi: 10.14569/IJACSA.2023.0140561.

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,” 3rd Int. Conf. Smart Electron. Commun. ICOSEC 2022 - Proc., no. October, pp. 225–230, 2022, doi: 10.1109/ICOSEC54921.2022.9952154.

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, pp. 1–22, 2021, doi: 10.3390/s21103478.

Yuliza, R. Muwardi, M. Rhozaly, M. Yunita, and G. Erica Yehezkiel, “Fast Human Recognition System on Real-Time Camera,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 4, pp. 895–903, 2023, doi: 10.26555/jiteki.v9i4.27009.

A. Hayat and F. Morgado-Dias, “Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety,” Appl. Sci., vol. 12, no. 16, 2022, doi: 10.3390/app12168268.

M. Ferdous and S. M. M. Ahsan, “PPE detector: A YOLO-based architecture to detect personal protective equipment (PPE) for construction sites,” PeerJ Comput. Sci., vol. 8, 2022, doi: 10.7717/peerj-cs.999.

F. Zhou, H. Zhao, and Z. Nie, “Safety Helmet Detection Based on YOLOv5,” Proc. 2021 IEEE Int. Conf. Power Electron. Comput. Appl. ICPECA 2021, pp. 6–11, 2021, doi: 10.1109/ICPECA51329.2021.9362711.

N. D. Nath, A. H. Behzadan, and S. G. Paal, “Deep learning for site safety: Real-time detection of personal protective equipment,” Autom. Constr., vol. 112, no. July 2019, 2020, doi: 10.1016/j.autcon.2020.103085.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Yoga Nanda Khoiril Umat, Wawan Gunawan

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