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

Luqman Ali, Fady Alnajjar, Medha Mohan Ambali Parambil, Mohammad Issam Younes, Ziad Ismail Abdelhalim, Hamad Aljassmi

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Article number8820
Issue number22
Publication statusPublished - Nov 2022


  • IoT
  • PPE compliance
  • YOLOv5
  • deep learning
  • object detection
  • safety
  • smart academic laboratories

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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