Face Mask Classification Based on Deep Learning Framework
Safa Teboulbi, Seifeddine Messaoud, Mohamed Ali Hajjaji, Abdellatif Mtibaa · Advances in web technologies and engineering book series · 2022
Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem around the world. This pandemic is having devastating effects on societies and economies. Due to the lack of health resources in a short period, all countries and continents are likely to face particularly severe damage that could lead to a large epidemic. Wearing a face mask that stops the transmission of droplets in the air can still be helpful in combating this pandemic. Therefore, this chapter focuses on implementing a face mask detection model as an embedded vision system. The six pre-trained models, which are MobileNet, ResNet-50, MobileNet-V2, VGG-19, VGG-16, and DenseNet, are used in this context. People wearing or not wearing masks were detected. After implementing and deploying the models, the selected models achieved a confidence score. Therefore, this study concludes that wearing face masks helps reduce the virus spread and fight this pandemic.