COVID-19 pandemic has caused widespread political and financial instability across the world. The WHO has released several recommendations for coronavirus control. One of the best preventive measures is wearing a face mask in public places. According to research, wearing a face mask significantly decreases the chance of infection. As a result, a face mask detection deep learning model can be used to detect either individuals are wearing a mask or not, allowing the system to reduce the number of enforcing agents on the ground. Using the YOLOv4 architecture, the system is trained to capture facial mask features in images and real-time streams. After training 4000 epochs, the YOLOv4 object detection algorithm achieved remarkable results in real-time scenarios, with an average FPS of 49.5 and a mAP of 98%. © 2021 IEEE.
Real-time Covid-19 Face Mask Detection with YOLOv4