Convolutional Neural Network for Identification of Personal Protective Equipment Usage Compliance in Manufacturing Laboratory

Khania O.P.P. Nugraha(1*), Achmad Pratama Rifai(2),

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(*) Corresponding Author


Data from the Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan Indonesia from 2019 to 2021 shows that the number of work accident victims who claimed Work Accident Insurance (Jaminan Kecelakaan Kerja / JKK) continues to increase. The high number of work accidents is mostly caused by unsafe behavior at work sites, one of which is in terms of compliance with the use of Personal Protective Equipment (PPE). One tool that is considered important as a step in reducing work accidents is an identification system for compliance of personal protective safety equipment use that can detect PPE used by visitors or workers. This study develops an automatic identification system that is built using the Convolutional Neural Network (CNN) to identify the use of PPE in the manufacturing technology laboratory. The CNN models used are the 4th and 5th versions of You Only Look Once (YOLO) which are then compared based on two methods: train from scratch and transfer learning. The dataset used for building the detection system has 11,579 images consisting of six classes of PPE objects. Overall performance of the proposed models shows very good results. Moreover, the comparison result among the three models shows that YOLOv5 transfer learning has the best performance with the best precision (94.2 %), recall (91.8 %), and mAP (88.6%).


personal protective equipment; Convolutional Neural Network; deep learning; object detection; YOLO

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Barro-Torres, S.; Fernández-Caramés, T.M.; Pérez-Iglesias, H.J.; Escudero, C.J. (2012). “Real-time Personal Protective Equipment Monitoring System”, Computer Communications, Vol. 36(1), 42–50.

Bochkovskiy, A.; Wang, C.-Y.; Liao, H.-Y. M. (2020). “YOLOv4: Optimal Speed and Accuracy of Object Detection”, arXiv preprint: 2004.10934

BPJS Ketenagakerjaan. (2021). Laporan Keuangan dan Pengelolaan Program BPJS Ketenagakerjaan 2020.

BPJS Ketenagakerjaan. (2022). Laporan Keuangan dazn Pengelolaan Program BPJS Ketenagakerjaan 2021.

Chen, S.; Demachi, K. (2020). “A Vision-based Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Decommissioning of Fukushima Daiichi Nuclear Power Station”, Applied Sciences, Vol. 10(15), 1–14.

Colares, R.A.L.; de Alencar, D.B.; Junior, J.D.A.B.; da Cruz, J.C.; Bezerra, C.M.V.O. (2019). “The Importance of PPE Use in Civil Construction: Case Study”, Journal of Engineering and Technology for Industrial Applications, Vol. 5(20).

de Oliveira, C.S.; Sanin, C.; Szczerbicki, E. (2018). “Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning”, Cybernetics and Systems, Vol. 49(5–6), 355–367.

Delhi, V.S.K.; Sankarlal, R.; Thomas, A.; (2020). “Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques”, Frontiers in Built Environment, Vol. 6(September).

Ding, L.; Fang, W.; Luo, H.; Love, P.E.D.; Zhong, B.; Ouyang, X. (2018). “A Deep Hybrid Learning Model to Detect Unsafe Behavior: Integrating Convolution Neural Networks and Long Short-Term Memory”, Automation in Construction, Vol. 86, 118–124.

Fang, Q.; Li, H.; Luo, X.; Ding, L.; Luo, H.; Rose, T.M.; dan An, W. (2018). “Detecting Non-hardhat-use by a Deep Learning Method from Far-field Surveillance Videos”, Automation in Construction, Vol. 85, 1–9.

Ge, Z.; Liu, S.; Wang, F.; Li, Z.; Sun, J. (2021). “YOLOX: Exceeding YOLO Series in 2021”, 1–7.

Hung, P.D.; Su, N.T. (2021). “Unsafe Construction Behavior Classification Using Deep Convolutional Neural Network”, Pattern Recognition and Image Analysis, Vol. 31(2), 271–284.

Kasper-Eulaers, M.; Hahn, N.; Kummervold, P. E.; Berger, S.; Sebulonsen, T.; Myrland, Ø. (2021). “Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5”, Algorithms, Vol. 14(4).

Krizhevsky, B. A.; Sutskever, I.; Hinton, G. E. (2017). “ImageNet Classification with Deep Convolutional Neural Networks”, Communications of the ACM, Vol. 60(6), 84–90.

Lecun, Y.; Bottou, L.; Bengio, Y.; Ha, P. (1998). “Gradient-Based Learning Applied to Document Recognition”, Proceedings of the IEEE, 1–46.

Li, Y.; Wei, H.; Han, Z.; Huang, J.; Wang, W. (2020). “Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks”, Advances in Civil Engineering, 2020.

Liu, H.; Fan, K.; Ouyang, Q.; Li, N. (2021). “Real-time Small Drones Detection Based on Pruned YOLOv4”, Sensors, Vol. 21(10).

Luo, X.; Li, H.; Wang, H.; Wu, Z.; Dai, F.; Cao, D. (2019). “Vision-based Detection and Visualization of Dynamic Workspaces”, Automation in Construction, Vol. 104, 1–13.

Nath, N.D.; Behzadan, A.H.; Paal, S.G. (2020). “Deep Learning for Site Safety: Real-time Detection of Personal Protective Equipment”, Automation in Construction, Vol. 112, 103085.

Önal, O.; Dandıl, E. (2021). “Object Detection for Safe Working Environments using YOLOv4 Deep Learning Model”, European Journal of Science and Technology, Vol. 26, 343–351.

Padilla, R.; Netto, S.L.; Da Silva, E.A.B. (2020). “A Survey on Performance Metrics for Object-Detection Algorithms”, International Conference on Systems, Signals, and Image Processing, 237–242.

Padilla, R.; Passos, W.L.; Dias, T.L.B.; Netto, S.L.; Da Silva, E.A.B. (2021). “A Comparative Analysis of Object Detection Metrics with a Companion Open-source Toolkit”, Electronics, Vol. 10(3), 1–28.

Patterson, J.; Gibson, A. (2017). Deep Learning: A Practitioner’s Approach, In O’Reilly.

Rahman, E.U.; Zhang, Y.; Ahmad, S.; Ahmad, H.I.; Jobaer, S. (2021). “Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs”, Sensors, Vol. 21(3), 1– 24.

Redmon, J.; Divvala, S.; Girshick, R.; dan Farhadi, A. (2016). “You Only Look Once: Unified, Real-Time Object Detection”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 779– 788.

Wang, Z.; Wu, Y.; Yang, L.; Thirunavukarasu, A.; Evison, C.; Zhao, Y. (2021). “Fast personal protective equipment detection for real construction sites using deep learning approaches”, Sensors, 21(10), 3478.

Wu, H. Zhao, J. (2018). “Automated Visual Helmet Identification Based on Deep Convolutional Neural Networks”, Computer Aided Chemical Engineering, Vol. 44(2018), 2299–2304.

Zhafran, F.; Ningrum, E.S.; Tamara, M.N.; Kusumawati, E. (2019). “Computer Vision System Based for Personal Protective Equipment Detection, by Using Convolutional Neural Network”, Proceedings of IES 2019 - International Electronics Symposium: The Role of Techno-Intelligence in Creating an Open Energy System Towards Energy Democracy, 516–521.

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