Facemask Detection to Prevent COVID-19 Using YOLOv4 Deep Learning Model

The on-going global Covid-19 pandemic has impacted everyone’s life. World Health organization (WHO) and Governments all over world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of homes in a face mask is a social obligation and a law mandate that is often violated by people and hence a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, YOLOv4 deep learning model is designed and applied deep transfer learning approach to create a face mask detector which can be used in real time. GPU used was Google Collab to run the simulations and to draw inferences. Proposed implementation considered three types of data as input such as image dataset, video dataset and real time data for face mask detection. Performance parameters are tabulated and obtained mean average precision of 0.86, F1 score 0.77 for image dataset, 90 % accuracy for video dataset. And real time face mask detector with accuracy of 95%, it is successfully able to identify a person with and without facemask and report if they are wearing a face mask or not.