Saadia Binte Alam, PhD
- Director, MIRA & WiSE Wings
Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
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)
In: 2023 International Conference on Machine Learning and Cybernetics (ICMLC)
IEEE, pp. 242-247
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
Personal protective equipmentHealth careComputer scienceMedical equipmentDeep learningArtificial intelligenceRisk analysis (engineering)
CCDS Authors
Md. Rashedur Rahman, D. Eng.
- Co-Director, MIRA & NEST Wings
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Prof. M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Professor
+1 more affiliationDepartment of Information Sciences and Technology
George Mason University, USA
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
References
- 1.Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C. Lawrence Zitnick. (2014). Microsoft COCO: Common Objects in Context. Lecture notes in computer science, 740–755[10.1007/978-3-319-10602-1_48]
- 2.Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. , 7464–7475[10.1109/cvpr52729.2023.00721]
- 3.Jos Verbeek, Blair Rajamaki, Sharea Ijaz, Riitta Sauni, Elaine Toomey, Bronagh Blackwood, Christina Tikka, Jani Ruotsalainen, F Selcen Kilinc Balci. (2020). Personal protective equipment for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff. Cochrane Database of Systematic Reviews, 4, CD011621[10.1002/14651858.cd011621.pub4]
- 4.Nipun D. Nath, Amir H. Behzadan, Stephanie German Paal. (2020). Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction, 112, 103085[10.1016/j.autcon.2020.103085]
- 5.Benedetta Allegranzi, Claire Kilpatrick, Julie Storr, Edward Kelley, Benjamin J. Park, Liam Donaldson. (2017). Global infection prevention and control priorities 2018–22: a call for action. The Lancet Global Health, 5(12), e1178–e1180[10.1016/s2214-109x(17)30427-8]
- 6.Philip L. Russo, Andrew J. Stewardson, Allen Cheng, Tracey Bucknall, Brett Mitchell. (2019). The prevalence of healthcare associated infections among adult inpatients at nineteen large Australian acute-care public hospitals: a point prevalence survey. Antimicrobial Resistance and Infection Control, 8(1), 114[10.1186/s13756-019-0570-y]
- 7.Adriana van Buijtene, Dona Foster. (2018). Does a hospital culture influence adherence to infection prevention and control and rates of healthcare associated infection? A literature review. Journal of Infection Prevention, 20(1), 5–17[10.1177/1757177418805833]
- 8.Saurav Kumar, Himanshu Gupta, Drishti Yadav, Irshad Ahmad Ansari, Om Prakash Verma. (2021). YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites. Multimedia Tools and Applications, 81(16), 22163–22183[10.1007/s11042-021-11280-6]
- 9.Adban Akib Protik, Amzad Hossain Rafi, Shahnewaz Siddique. (2021). Real-time Personal Protective Equipment (PPE) Detection Using YOLOv4 and TensorFlow. 2021 IEEE Region 10 Symposium (TENSYMP), 1–6[10.1109/tensymp52854.2021.9550808]
- 10.Baizheng Wu, Chengxin Pang, Xinhua Zeng, Xing Hu. (2022). ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment. Applied Sciences, 12(23), 11978[10.3390/app122311978]
- 11.Ruth Barratt, Mary Wyer, Su-yin Hor, Gwendolyn L. Gilbert. (2020). Medical interns’ reflections on their training in use of personal protective equipment. BMC Medical Education, 20(1), 328[10.1186/s12909-020-02238-7]
- 12.Ng Wei Bhing, Patrick Sebastian. (2021). Personal Protective Equipment Detection with Live Camera. , 221–226[10.1109/icsipa52582.2021.9576811]
- 13.Thomas Karopka, Gudrun Mernitz, Mads Lause Mogensen, Stefan Reich, Rikard Lövström, Hercules Dalianis, H.A. Leslie, Michael Behnke. (2016). Minimum data set and standards in the context of nosocomial infection surveillance and antimicrobial stewardship.
- 14.Shayan Khosravipour, Erfan Taghvaei, Nasrollah Moghadam Charkari. (2021). COVID-19 personal protective equipment detection using real-time deep learning methods. arXiv (Cornell University)[10.48550/arxiv.2103.14878]
- 15.Rishit Dagli, Ali Mustufa Shaikh. (2021). CPPE-5: Medical Personal Protective Equipment Dataset. arXiv (Cornell University)[10.48550/arxiv.2112.09569]
Cited By
- 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.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.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.Xia Hua, Lei Han. (2024). Frequency-Domain Guided Image Classification With Large Model Assistance. IEEE Access, 12, 186246–186254[10.1109/access.2024.3500099]