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Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students work...

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Autores principales: Ali, Luqman, Alnajjar, Fady, Parambil, Medha Mohan Ambali, Younes, Mohammad Issam, Abdelhalim, Ziad Ismail, Aljassmi, Hamad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699226/
https://www.ncbi.nlm.nih.gov/pubmed/36433418
http://dx.doi.org/10.3390/s22228820
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author Ali, Luqman
Alnajjar, Fady
Parambil, Medha Mohan Ambali
Younes, Mohammad Issam
Abdelhalim, Ziad Ismail
Aljassmi, Hamad
author_facet Ali, Luqman
Alnajjar, Fady
Parambil, Medha Mohan Ambali
Younes, Mohammad Issam
Abdelhalim, Ziad Ismail
Aljassmi, Hamad
author_sort Ali, Luqman
collection PubMed
description The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.
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spelling pubmed-96992262022-11-26 Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions Ali, Luqman Alnajjar, Fady Parambil, Medha Mohan Ambali Younes, Mohammad Issam Abdelhalim, Ziad Ismail Aljassmi, Hamad Sensors (Basel) Article The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students. MDPI 2022-11-15 /pmc/articles/PMC9699226/ /pubmed/36433418 http://dx.doi.org/10.3390/s22228820 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ali, Luqman
Alnajjar, Fady
Parambil, Medha Mohan Ambali
Younes, Mohammad Issam
Abdelhalim, Ziad Ismail
Aljassmi, Hamad
Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title_full Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title_fullStr Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title_full_unstemmed Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title_short Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions
title_sort development of yolov5-based real-time smart monitoring system for increasing lab safety awareness in educational institutions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699226/
https://www.ncbi.nlm.nih.gov/pubmed/36433418
http://dx.doi.org/10.3390/s22228820
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