Utilization of Convolutional Neural Network (CNN) to Build a Camera-Based Personal Protective Equipment (PPE) Detection System
DOI:
https://doi.org/10.55927/marcopolo.v3i6.71Keywords:
CNN, Deep Learning, Object Detection, Personal Protective Equipment, Camera, YOLOAbstract
Occupational safety is a critical aspect in the industrial sector. One important implementation of safety standards is the use of Personal Protective Equipment (PPE). However, manual monitoring is often ineffective and requires significant human resources. This research aims to develop an automated PPE detection system using Deep Learning technology, specifically a Convolutional Neural Network (CNN) architecture, integrated with a camera. The detection model is built using the YOLOv5 algorithm, which is based on CNN principles, and trained with a dataset containing images of workers with and without PPE. The system was tested in a simulated work environment using real-time camera input. Evaluation results show that the model achieved an accuracy of XX%, indicating that the system can serve as an effective tool in supporting workplace safety and compliance.
References
A. Anggraini and H. Zakaria, "Penerapan Metode Deep Learning Pada Aplikasi Pembelajaran Menggunakan Sistem Isyarat Bahasa Indonesia Menggunakan Convolutional Neural Network (Studi Kasus: SLB-BC Mahardika Depok)," 2023.
A. Meirza and N. R. Puteri, "Implementasi Metode YOLOV5 dan Tesseract OCR untuk Deteksi Plat Nomor Kendaraan," 2024.
K. Azmi, S. Defit, and Sumijan, "Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat," 2023.
M. Tarmizi and Yahfizham, "Perspektif Mahasiswa Terhadap Penggunaan Kecerdasan Buatan ChatGPT dalam Penyusunan Tugas Akhir," 2024.
R. Rizal, Martanto, and Y. A. Wijaya, "Analisa Dataset Software Defined Network Intrusion Menggunakan Algoritma Deep Learning H2O," 2022.
