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:口罩被推荐用于减少病毒传播,尤其是SARS-CoV-2。因此,自动检测面部上是否戴着口罩、口罩类型是什么以及佩戴方式的可能性是一个重要的研究课题。在本文中,考虑使用热成像来分析在脸部检测(定位)口罩的可能性,以及检查是否能够对脸部的口罩类型进行分类。已经提出的热成像数据集进行了扩展,并注释了口罩类型和脸部口罩位置的描述。不同的深度学习模型被应用和改进。用于口罩检测的最佳模型是nano版的Yolov5模型,mAP高于97%,精度约为95%。口罩类型分类也获得了高精度。使用先前训练在热成像重建问题上的自编码器建立的卷积神经网络模型获得了最佳结果。预训练的编码器用于训练分类器,精度达到91%。