A real-time model for COVID19 face-mask identification with 'YOLOv4'

ABSTRACT

At the beginning of 2020 WHO declared COVID19 as an epidemic;healthcare industries experts and academicians from worldwide are working in the directions to surveillance the daily behaviors of the citizens to combat the COVID-19 cases. In India, we thank the government for performing its outperformed active measures and spontaneous compliance to follow the policy of wearing masks when moving out to any public places;it entails active real-time monitoring to supervise the citizens by governments. In this process, real-time face-mask identification is a very challenging task of computer vision. And the absence of accurate datasets for this problem is a critical hard problem to solve. To address this bottleneck, we are proposing our real-time deep learning face-mask identification technique with annotated class labels with bounding boxes which have its real-time application to assist the governments to control and prevent the spread of these epidemics in its supervision. Our model is very robust and effective to classify the real-time images and videos for face mask detection with accuracy and average precision. The proposed model substitutes the manual surveillance with the object detection method using YOLOv4 supported on a deep learning approach to monitor the crowd accurately even if they change their respective locations. The experiment identify or classify the object within any dataset to distinguish the images or videos with two class labels such as 'with-mask' and 'without-mask' with approximately 98.26% accuracy, mAP of 68.28%, recall of 77%, and precision of 57%. © 2022 IEEE.