Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment

• Published in: Multimedia tools and applications Vol. 80; no. 13; pp. 19753 - 19768
• Main Authors: Singh, Sunil, Ahuja, Umang, Kumar, Munish, Kumar, Krishan, Sachdeva, Monika
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data Structures and Information Theory; Disease control; Environment models; Masks; Multimedia Information Systems; Object recognition; Special Purpose and Application-Based Systems; Viral diseases; COVID-19; Deep learning; YOLO v3; Face mask detection; Faster R-CNN
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-10711-8

There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.