Detection of COVID-19 Pandemic Face Mask Using ConvNet in Busy Environments

Abstract

The number of people using face masks has increased on public transportation, retail outlets, and at the workplace. All municipal entrances, workplaces, malls, schools, and hospital gates must have temperature and mask checks in order for people to enter. The paper's goal is to find someone who isn't wearing a face mask in order to control COVID-19. ConvNets may be used to recognize and classify images. The model depends on ConvNot to assess whether or not someone is wearing a mask. It is possible to identify an image's face by utilizing a face identification algorithm. These faces are then processed using Conv Net face mask detection. If the model is able to extract patterns and characteristics from photographs, it will be categorized as either “Mask” or “No Mask”. With an accuracy rate of 99.85 percent, Mobile Net V2 is the most accurate in regard to training data. MobilenetV2 correctly identifies the mask in “Mask” or “No Mask” video transmissions.<br>