Identification of Face Mask and Social Distancing using YOLO Algorithm based on Machine Learning Approach
Rupali Kolpe, Shubham Ghogare, M. A. Jawale, P. William, A. B. Pawar
Abstract
An outbreak of the coronavirus disease due to occur in 2020 has already had a significant impact on the human race. Wearing a "Face Mask" and maintaining "Social Distancing" are the only means to protect ourselves from this pandemic. Several service providers, such as airlines, hotels, hospitals, and train stations, demand their customers to access the service only if the Mask is worn correctly and social distance is maintained. Manually checking to see if the rule of mask wearing and social distance is being observed is impossible due to the significant human resource consumption. As a one-stage detector, the COVID-19 Face Mask and Social Distancing Detector System uses an artificial neural network to combine high-level semantic information with various feature maps and a machine learning module to identify face masks and social distances at the same time. It will also be able to detect persons without masks and the violence of social separation by using existing IP cameras, CCTV cameras, and computer vision. This technology eliminates the need for a manual surveillance system by providing instruments for safety and security. The technology can be used in any type of infrastructure, including hospitals, government offices, schools, and construction sites. Therefore, the face mask and social distance detector system developed, could aid to secure the protection and security of ourselves and our loved ones.