Journal Article2023Cited by 4

Medical Personal Protective Equipment detection using YOLOv7

Md Anas Ali, Mahmudul Haque, Saadia Binte Alam, Rashedur Rahman, M Ashraful Amin, Syoji Kobashi

2023 International Conference on Machine Learning and Cybernetics (ICMLC)

IEEE, pp. 242-247

  • Good Health and Well-being — Relevance: 89%
DOI: 10.1109/icmlc58545.2023.10327939URL

Abstract

In recent years, healthcare and safety have been a major focus of deep learning research. This paper focuses on the detection of Medical Personal Protective Equipment (MPPE) in the health-care sector using YOLOv7. Improper use of personal protective equipment (PPE) can result in the contamination and cross-contamination of infectious diseases, so it is crucial for healthcare professionals to use it correctly. The CPPE-5 dataset was used to train the model, which contains 1029 high-quality images divided into five categories: coveralls, face shield, gloves, masks, and goggles. The objective of this research is to create an accurate model for future applications and development using a suitable medical PPE dataset. The proposed model outperforms previous studies, with an optimal mAP of 90.93 %, indicating that it is a promising method for detecting MPPE in the healthcare sector.

Keywords

  • YOLOv7
  • PPE detection
  • gloves
  • cross-contamination
  • coveralls
  • goggles
  • Object detection
  • face shields
  • masks
  • healthcare workers

CCDS Authors

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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Cited By

  1. 1.Giustino Claudio Miglionico, Pietro Ducange, Francesco Marcelloni, Carlo Vallati, Francesco Di Rienzo. (2024). Performance Evaluation of YOLOv5 on Edge Devices for Personal Protective Equipment Detection. , 1–8[10.1109/ijcnn60899.2024.10651429]
  2. 2.Anup Kumar Paul. (2024). FaceLite: A Real-Time Light-Weight Facemask Detection Using Deep Learning: A Comprehensive Analysis, Opportunities, and Challenges for Edge Computing. Computer Networks and Communications[10.37256/cnc.2120244439]
  3. 3.Shenwang Li, Yuyang Zhou, Minjie Wang, Li Liu, Thomas Wu. (2025). MRC-DETR: A High-Precision Detection Model for Electrical Equipment Protection in Power Operations. Sensors, 25(13), 4152[10.3390/s25134152]
  4. 4.Xia Hua, Lei Han. (2024). Frequency-Domain Guided Image Classification With Large Model Assistance. IEEE Access, 12, 186246–186254[10.1109/access.2024.3500099]
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