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.

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As of March 31, 2021, the Coronavirus COVID-19 was affecting 219 countries and territories worldwide, with approximately 129,574,017 confirmed cases and 2,830,220 death cases. Social isolation is the most reliable way to deal with this pandemic situation. Motivated by this notion, this paper proposes a deep learning-based technique for automating the task of monitoring social distancing using surveillance cameras. To separate humans from the background, the proposed system employs object detection models based on F-RCNN (Faster Region-based Convolutional Neural Networks) and YOLO (You Only Look Once) algorithms. In the COVID-19 environment, these models track the percentage of people who violate social distancing norms on a daily basis. The authors compared the performance of both models in experimental work using the MS COCO dataset. Many tests were carried out, and we discovered that YOLOv3 demonstrated efficient performance with balanced FPS (frames per second).