A Survey on Face Mask Detection and Social Distancing Measurement Using Machine Learning

Abstract

Abstract: The purpose of the project “Implementation of Covid-19 Monitoring System using ML-based Face Mask Detectionand Social Distancing Measurement” is to create a tool that identifies the image of a human that can calculate the probability that he/she is wearing a mask or not and another motive of our project is to detect the people and check whether they aremaintaining the social distancingnorms. Using Machine Learning and Object Detection, this study seeks to detect face masksand social separation. The contribution is a strategy for merging more complex classifiers in a "cascade" that allows the image's background areas to be swiftly rejected while more computation is spent on promising object-like regions. While deep learning-based methods for general object detection have progressed dramatically in the previous two years, most approaches to face detection still use the CNN framework, which results in low accuracy and processing performance. We examine the use of MobileNet(pretrained model) and feature extraction in this project, which will yield outstanding results on a variety of object detection benchmarks. YOLO Object detection was used to detect persons in a frame and calculate the distance between them to check for social distancing. As a result, in a real-time situation, this technology tracks persons wearing or not wearing masks and provides social separation by triggering an alert if there is a violation in the scene or in public spaces. This can be combined with current embedded camera infrastructure to allow these analytics, which can be used in a variety of verticals as well as in offices and airport terminals/gates. Keywords: COVID–19, Face Mask, Social Distancing, Machine Learning,MobileNet, YOLOv3, Tensorflow, Keras, OpenCV, Object Detection. Abbreviations: DNN (Deep Neural Network), YOLOv3 (You Only Look Once Version3)