Social Distance and Face Mask Detection using...

Social Distance and Face Mask Detection using MobileNetV2 and VGG19: a Survey

Author Name : G. Vinay Deekshit, Ch. Bhragav, A. Geetanjali, K. Susmitha, I. Divya

ABSTRACT

The COVID-19 epidemic, which has recently made news, is causing a problem on a worldwide scale that will unavoidably expand. The WHO advises taking preventative measures like wearing a face mask and keeping your distance. The COVID-19 infection causes respiratory issues and shortness of breath in those who contract it. The concerned persons' droplets that carry the virus can contaminate their surroundings. Although wearing a mask and maintaining physical distance are required, many people disregard the rules. In situations like this, it's usual practice to regularly check for face masks in public settings and to levy fines.Object detection has been widely used in surveillance, security, autonomous driving, and other fields as it has developed into a practicable biometric procedure. Face mask detectors are well suited to create social distance thanks to the rapid advancement of deep learning models, and object detectors can control crowds using CCTV and security cameras. To create such detectors, the research examines a number of deep learning networks. The current object detection models used for surveillance and people detection are examined in this survey. The performance and applications of the one-stage and two-stage detectors are comprehensively described. We explore and contrast deep learning models including YOLOv3, Resnet-50, vgg16, vgg19, MObileNetv2, and Faster RCNN.

Keywords- Deep Learning, YOLOV3, Res Net50, vgg16, vgg19, Mobile NetV2, Faster RCNN, Social Distancing and Face mask detection