Personal Protective Equipment Detection with Live Camera

Abstract

With the recent outbreak and rapid transmission of COVID-19, medical personal protective equipment (PPE) detection has seen significant importance in the domain of computer vision and deep learning. The need for the public to wear face masks in public is ever increasing. Research has shown that proper usage of face masks and PPE can significantly reduce transmission of COVID-19. In this paper, a computer vision with a deep-learning approach is proposed to develop a medical PPE detection algorithm with real-time video feed capability. This paper aims to use the YOLO object detection algorithm to perform one-stage object detection and classification to identify the three different states of face mask usage and detect the presence of medical PPE. At present, there is no publicly available PPE dataset for object detection. Thus, this paper aims to establish a medical PPE dataset for future applications and development. The YOLO model achieved 84.5% accuracy on our established PPE dataset comprising seven classes in more than 1300 images, the largest dataset for evaluating medical PPE detection in the wild.