Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.

翻译：佩戴口罩被推荐用于减少多种病毒的传播，尤其是SARS-CoV-2。因此，自动检测面部是否佩戴口罩、所戴口罩类型以及佩戴方式是一个重要的研究课题。本研究采用热成像技术，分析检测（定位）面部口罩的可能性，并验证是否可以对面部口罩类型进行分类。我们对先前提出的热成像数据集进行了扩展，并标注了口罩类型及口罩在面部的具体位置。研究采用了多种深度学习模型。结果显示，Yolov5模型的“nano”版本在口罩检测方面表现最佳，其平均精度（mAP）超过97%，精确率约为95%。在口罩类型分类任务中，我们也获得了高准确率。最佳结果来自基于自编码器的卷积神经网络模型，该自编码器最初在热图像重建问题上进行训练。利用预训练的编码器训练分类器，最终实现了91%的准确率。