Қазақстанның мұнай-газ саласының хабаршысы

2707-42262957-806X

KMG Engineering

109018

10.54859/kjogi109018

Digital monitoring of the use of personal protective equipment at industrial facilities using neural network architectures

Abdimanap

Galymzhan S.

g.abdimanap@kmge.kzhttps://orcid.org/0000-0003-1676-4075Alimova

Anel N.

PhDa.alimova@kmge.kzhttps://orcid.org/0000-0002-5155-2417Bostanbekov

Kairat A.

PhDk.bostanbekov@kmge.kzhttps://orcid.org/0000-0003-2869-772XAbdrakhmanov

Renat E

r.abdrakhmanov@kmge.kzhttps://orcid.org/0009-0004-1997-9818Orenkyzy

Dana

d.orenkyzy@kmge.kzhttps://orcid.org/0009-0000-2265-2091Nurseitov

Daniyar B.

Cand. Sc. (Physics and Mathematics), professor (associate)d.nurseitov@kmge.kzhttps://orcid.org/0000-0003-1073-4254

KMG EngineeringSatbayev University83

22062026

30072026

ABSTRACT Relevance. Ensuring employees occupational safety at the oil and gas industry facilities remains a relevant objective, as far as traditional monitoring methods for the use of Personal Protective Equipment (PPE) are based on manual visual inspections and are susceptible to human factor. Most existing computer vision systems are limited to detecting only a small number of 2–6 classes of PPE categories and to verifying the anatomical consistency between detected protective equipment and the corresponding body parts of employees. Study purpose. Developing and validating a digital monitoring method for compliance with PPE use requirements based on neural network architectures, integrating algorithms for object detection, human pose estimation, and anatomical matching of PPE elements in real time mode. Materials and Methods. A unique dataset of 16 352 images (after augmentation) containing 13 object classes, including 6 types of PPE and 6 negative classes was created for training the model. The YOLOv8 model was used for object detection, and HRNet for human pose estimation. A two-tier video stream processing architecture was implemented, combining object tracking (BoT-SORT), spatial and anatomical matching and TensorRT quantization to improve system performance. Results. During the training phase, the YOLOv8 model achieved Precision = 0.98, Recall = 0.97, and F1 = 0.94. When testing the developed system on 16 video files obtained from industrial sites, the system achieved a precision of 96.58%, a recall of 68.48%, and an F1 score of 0.8014. Detecting small objects (gloves) in cropped images improves detection efficiency by 2-3 times compared to full-frame processing. Conclusion. The developed approach, combining the YOLOv8 and HRNet models and an anatomical matching algorithm, provides effective digital monitoring of compliance with PPE requirements in real-world production conditions. The obtained results confirm the potential of its application in the development of intelligent industrial safety monitoring systems at industrial facilities.

industrial safety, personal protective equipment (PPE), computer vision, YOLOv8 neural network, HRNet, human pose estimation, object detection.

өндірістік қауіпсіздік, жеке қорғаныс құралдары (ЖҚҚ), компьютерлік көру, YOLOv8 нейрондық желісі, HRNet, адамның позасын бағалау, объектілерді детекциялау.

производственная безопасность, средства индивидуальной защиты (СИЗ), компьютерное зрение, нейросеть YOLOv8, HRNet, оценка позы человека, детекция объектов

1. Kelm A., Laußat L., Meins-Becker A., Platz D., et al., Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites // Automation in Construction. 2013. Vol. 36, P. 38–52. doi: 10.1016/j.autcon.2013.08.009

2. Zhang H., Yan X., Li H., Jin R. and Fu H. Real-Time Alarming, Monitoring, and Locating for Non-Hard-Hat Use in Construction // Journal of Construction Engineering and Management. 2019. Vol. 145, P.1–13. doi: https://doi.org/10.1061/(ASCE)CO.1943-7862.000162

3. Dubey S. Dixit M. A comprehensive survey on human pose estimation approaches // Multimedia Systems. 2022. Vol. 29, P. 167–195. doi: 10.1007/s00530-022-00980-0

4. Xiong R., Tang P. Pose guided anchoring for detecting proper use of personal protective equipment // Automation in Construction. 2021. Vol.130, Article 103828. 17 p. doi: https://doi.org/10.1016/j.autcon.2021.103828.

5. Chen S. and Demachi K. A vision-based approach for ensuring proper use of personal protective equipment (PPE) in decommissioning of Fukushima Daiichi nuclear power station // Applied Sciences. 2020. Vol.10, Article 5129. 14 p. doi: https://doi.org/10.3390/app10155129.

6. Delhi V.S.K., Sankarlal R. and Thomas A. Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques // Frontiers in Built Environment. 2020. Vol. 6, Article 136. 10 p. doi: https://doi.org/10.3389/fbuil.2020.00136.

7. Wang Z., Wu Y., Yang L., Thirunavukarasu A., Evison C. and Zhao Y. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches // Sensors. 2021. Vol. 21, Article 3478. 22 p. doi: https://doi.org/10.3390/s21103478.

8. GitHub [Internet]. ZijianWang Z. PPE_detection [cited 2026 Мау 5]. Available from https://github.com/ZijianWang-ZW/PPE_detection.

9. Ma L., Li X., Dai X., Guan Z., Lu Y. A Combined Detection Algorithm for Personal Protective Equipment Based on Lightweight YOLOv4 Model // Wireless Communications and Mobile Computing. 2022. Vol. 2022. Article ID 3574588. 11 p. doi: https://doi.org/10.1155/2022/3574588.

10.

10. Lo J.-H., Lin L.-K., and Hung C.-C. Real-Time Personal Protective Equipment Compliance Detection Based on Deep Learning Algorithm // Sustainability. 2023. Vol. 15, Article 391. 15 p. doi: https://doi.org/10.3390/su15010391.

11.

11. Lee Y.-R., Jung S.-H., Kang K.-S., Ryu H.-C., Ryu H.-G. Deep Learning-Based Framework for Monitoring Wearing Personal Protective Equipment on Construction Sites // Journal of Computational Design and Engineering. 2023. Vol. 10, P. 905–917. doi: https://doi.org/10.1093/jcde/qwad040.

12.

12. Nugraha K.O.P.P., Rifai A.P. Convolutional Neural Network for Identification of Personal Protective Equipment Usage Compliance in Manufacturing Laboratory // Jurnal Ilmiah Teknik Industri. 2023. Vol. 22, P. 11–24. doi: 10.23917/jiti.v22i1.21826.

13.

13. Zhao M., Barati M. Substation Safety Awareness Intelligent Model: Fast Personal Protective Equipment Detection Using GNN Approach // IEEE Transactions on Industry Applications. 2023. Vol. 59, P. 3142 - 3150. doi: 10.1109/TIA.2023.3234515

14.

14. Ngoc-Thoan N., Bui D.-Q. T., Tran C.N.N., and Tran D.-H. Improved Detection Network Model Based on YOLOv5 for Warning Safety in Construction Sites // International Journal of Construction Management. 2023. Vol. 24, P. 1007-1017. doi: https://doi.org/10.1080/15623599.2023.2171836.

15.

15. Wang H. Detection of Personal Protective Equipment (PPE) Using an Anchor Free-Convolutional Neural Network // International Journal of Advanced Computer Science and Applications. 2024. Vol. 15, 9 p. doi: 10.14569/IJACSA.2024.0150239

16.

16. Ren S., He K., Girshick R., Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017. Vol. 39, P. 1137–1149. doi: 10.1109/TPAMI.2016.2577031.

17.

17. Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C.-Y., Berg A.C. SSD: Single Shot MultiBox Detector // Computer Vision. 2016. Lecture Notes in Computer Science. Vol. 9905, Springer. P. 21–37. doi: 10.1007/978-3-319-46448-0_2.

18.

18. Redmon J., Divvala S., Girshick R., Farhadi A. You Only Look Once: Unified, Real-Time Object Detection // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. P. 779–788. doi: 10.1109/CVPR.2016.91.

19.

19. He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. P. 770–778. doi: https://doi.org/10.1109/CVPR.2016.90.

20.

20. Newell A., Yang K., Deng J. Stacked Hourglass Networks for Human Pose Estimation // Computer Vision. 2016. Lecture Notes in Computer Science. Vol. 9912, Springer. P. 483–499. doi: https://doi.org/10.1007/978-3-319-46484-8_29.

21.

21. GitHub [Internet]. CMU-Perceptual-Computing-Lab/openpose [cited 2026 Мау 5]. Available from https://github.com/CMU-Perceptual-Computing-Lab/openpose.

22.

22. Wei S.-E., Ramakrishna V., Kanade T., Sheikh Y. Convolutional Pose Machines // IEEE Conference on Computer Vision and Pattern Recognition. 2016. P. 4724–4732. doi: 10.1109/CVPR.2016.511.

23.

23. Sun K., Xiao B., Liu D., Wang J. Deep High-Resolution Representation Learning for Human Pose Estimation // IEEE Conference on Computer Vision and Pattern Recognition. 2019. P. 5686–5696. doi: 10.1109/CVPR.2019.00584.