Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection

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Published Dec 31, 2024
https://doi.org/10.37339/e-komtek.v8i2.2187
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Vol. 8 No. 2 (2024)

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Waluyo Nugroho
Politeknik Astra
Rifdah Zahabiyah
Politeknik Astra
Afianto
Politeknik Astra
Mada Jimmy Fonda Arifianto
Politeknik Astra

Abstract

The use of Personal Protective Equipment (PPE) is a critical step in ensuring worker safety in various sectors, including industry, construction, and health. However, violations in using PPE often occur, which can increase the risk of work accidents. This study aims to develop a deep learning-based PPE detection system using the YOLOv8 algorithm. This method was chosen because of its superior ability to detect objects in real time with high accuracy. The training data consists of various images of workers in different work environments, label to recognize types of PPE such as helmets, masks, and safety vests. The developed system was tested on a test dataset to evaluate model performance based on metrics such as confusion matrix, inference speed, and detection error rate. The experimental results show that the YOLOv8 model can detect PPE with an accuracy level of up to 95%. The implementation of this system is expected to be an effective solution in increasing compliance with the use of PPE and preventing work accidents.

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Creative Commons License

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

How to Cite
Nugroho, W., Rifdah Zahabiyah, Afianto, & Mada Jimmy Fonda Arifianto. (2024). Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection. Jurnal E-Komtek (Elektro-Komputer-Teknik), 8(2), 428-437. https://doi.org/10.37339/e-komtek.v8i2.2187

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