Mask Wearing Detection Based on Inceptionv4 and Multi-Scale Retinex Image Enhancement Algorithm

ABSTRACT

The novel coronavirus is a contagious virus with a high mortality rate, and the international health emergency due to COVID-19 has never stopped since the outbreak, due to this situation, wearing masks has become a basic recommended public epidemic prevention approach for many countries. In the current shortage of medical human resources, we urgently need a non-artificial mask-wearing detection method. In this paper, the multi-scale Retinex algorithm is involved as the preprocessing step of the input image. The mask-wearing detection is based on the InceptionV4 convolutional neural network model. During the experiment, we compared and verified the superiority of the Inception part of the InceptionV4 model compared with the Stem structure of the GoogLenet model, and from LFW (Labeled Faces in the Wild), RMFD (Real-World-Masked-Face-Dataset) more than 10,000 samples were selected from the public datasets for model training. Finally, the precise rate reaches 97.3 %.