A COVID-19 Safety Monitoring System: Personal Protective Equipment (PPE) Detection using Deep Learning

Medical personal protective equipment (PPE) identification has acquired attention in the computer vision and deep learning sectors as a result of COVID-19's recent breakthrough and quickly spread. The need for people to wear face masks in public is growing. COVID-19 transmission can be considerably reduced with the use of face masks and PPE, according to research. The goal of this research is to construct a medical PPE detection system using deep learning. The goal of this study is to utilize the YOLOv3 object detection method to conduct object detection, detecting whether a health worker is wearing complete medical PPE or not, upon entering wards or environments prone to the virus. In the study's findings, the detection model got a mean average precision score of 96.59%, detected complete and incomplete PPE varied with accuracies ranging from 40% to 80% which is to be expected given that there is a lot of variations of medical PPE with different colors and types.