Abstract
The disease was first discovered in Wuhan City, Hubei Province, the People’s Republic of China in late 2019, and rapidly grow to many countries around the world in early 2020, steadily turning into a global extensive pandemic. More than 222 million confirmed cases have been reported in different countries and regions around the world, and more than 4.6 million have died, which is one of the large-scale epidemics in human history . The coronavirus spreads through small droplets during the discussion, coughing, sneezing, etc. In poorly and closed ventilated locations a higher risk of transmission rate However, wearing a face mask that prevents the transmission of droplets in the air. But the continuous inspection of preventive measures both inside and outside the building/offices to prevent the growth of COVID-19 is a major challenging task. Therefore, in this research work, we focused on implementing a Face Mask Detection model that is relying on the related technologies of machine vision, we adopted three different well-known and the most advanced end-to-end target detection algorithm named CNN, VGG16, and -YOLOv5 to realize the detection and recognition of whether the face is wearing a mask. In terms of data set collection, we use the face mask opensource data set. After the actual effect test, we found the accuracy, error rate, recall rate, precision rate, and F1 of the Yolov5 algorithm model have reached a high level. This solution tracks the people with or without masks in a real-time scenario and highlighted the person with a red rectangle box in the case of violation. With the help of this 24/7, either inside or outside the organization continuously monitoring is possible and it has a great impact to identify the violator and ensure the safety of every individual.