Mask Detection for Covid-19 under Extreme Conditions

ABSTRACT

Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images. © 2022 IEEE.