Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention

Abstract

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploying the models, the selected one achieved a confidence score of 100%. This paper also provides a comparative study of different face detection and face mask classification models. The system performance is evaluated in terms of precision, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates.

Control and Prevention of SARS-CoV-2 Outbreaks among Healthcare Workers from 129 Healthcare Facilities in Mexico

“…Different artificial intelligence-related approaches have been tested to detect face mask use and body temperature measuring [39][40][41]. Some difficulties remain in that field, such as the detection of different mask types, different degrees of obstructions, among others [42].…”

ViDMASK dataset for face mask detection with social distance measurement

“…Transfer learning on state-of-art models has been widely used for multiple stage detectors like SSD with mobile net as well as single-state detectors like YOLOV3 and inception net. In [9] the authors used several object detection backbones to detect masks and perform social distance at different stages. However, a faster and more accurate model needs to be identified by leveraging the current deep learning models for real-time use.…”

“…Social distancing and mask detection have been implemented individually in several state-of-the-art publications. However, a pipeline for mask detection and social distancing is rare [9] .…”

AI Based Monitoring of Different Risk Levels in COVID-19 Context

“…Ref. [ 26 ] proposes the implementation of a facial mask and social distancing detection model as an embedded vision system. The pretrained models such as MobileNet, ResNet classifier, and VGG are used in our context.…”