Medical Personal Protective Equipment detection using YOLOv7

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 healthcare 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.