A COVID-19 Safety Monitoring System: Personal Protective Equipment (PPE) Detection using Deep Learning

Collo, Mark Lester R.; Esguerra, John Richard M.; Sevilla, Rovenson V.; Merin, Jovencio V.; Malunao, Dennis C.

doi:10.1109/dasa54658.2022.9765088

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Some studies have also developed models based on datasets from nuclear power plants, industrial workplaces, factories, and public spaces. Previous studies that conducted PPE detection, such as those research that focused on construction site PPE detection [6,8,13,19,20]. Meanwhile, research by [9], Refs.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories

Ludwika,

Rifai

2024

Safety

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Occupational sectors are perennially challenged by the potential for workplace accidents, particularly in roles involving tools and machinery. A notable cause of such accidents is the inadequate use of Personal Protective Equipment (PPE), essential in preventing injuries and illnesses. This risk is not confined to workplaces alone but extends to educational settings with practical activities, like manufacturing teaching laboratories in universities. Current methods for monitoring and ensuring proper PPE usage especially in the laboratories are limited, lacking in real-time and accurate detection capabilities. This study addresses this gap by developing a visual-based, deep learning system specifically tailored for assessing PPE usage in manufacturing teaching laboratories. The method of choice for object detection in this study is You Only Look Once (YOLO) algorithms, encompassing YOLOv4, YOLOv5, and YOLOv6. YOLO processes images in a single pass through its architecture, in which its efficiency allows for real-time detection. The novel contribution of this study lies in its computer vision models, adept at not only detecting compliance but also assessing adequacy of PPE usage. The result indicates that the proposed computer vision models achieve high accuracy for detection of PPE usage compliance and adequacy with a mAP value of 0.757 and an F1-score of 0.744, obtained with the YOLOv5 model. The implementation of a deep learning system for PPE compliance in manufacturing teaching laboratories could markedly improve safety, preventing accidents and injuries through real-time compliance monitoring. Its effectiveness and adaptability could set a precedent for safety protocols in various educational settings, fostering a wider culture of safety and compliance.

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