Mask Classification using Deep Learning Methods

ABSTRACT

During the COVID-19 pandemic, wearing gauze masks was proven to prevent people from infection. In public areas like shopping malls or schools need a way to supervise people wearing masks. This research aims to provide managers of public areas with an idea to solve this problem by GoogLeNet which is a type of convolutional neural network algorithm. Especially in crowded public areas, people should wear masks whether for their health or the health of others. These areas, such as stations and shopping malls, can only supervise people wearing masks at the entrance, but it is difficult to supervise people wearing masks inside buildings. As a result, many people will take off their masks or incorrectly wear them indoors due to heat. In this case, we consider how to intercept everyone's avatars in the video on closed-circuit television. Use neural network training algorithms to monitor everyone's mask-wearing situation. And promptly warn people who wear masks incorrectly or who do not wear masks. © 2022 SPIE.