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
DOI: https://doi.org/10.23917/jiti.v22i1.21826

Abstract

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%).

Keywords

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

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