PDF(Pubmed)
Abstract：
In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at \"https://github.com/BingshuCV/WMD.\" Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods.
摘要：
在2019年冠状病毒病（COVID-19）时代，戴口罩可以有效保护人们免受感染风险，并大大减少公共场所的传播，比如医院和机场。这带来了对检测戴口罩的人所需的监测仪器的需求。然而,这不是现有人脸检测算法的目标。在这篇文章中,我们提出了一个两阶段的方法来检测戴口罩使用混合机器学习技术。第一阶段旨在检测尽可能多的候选佩戴掩模区域，它基于Faster_RCNN和InceptionV2结构的传输模型，而第二阶段旨在使用广泛的学习系统来验证真实的面膜。它是通过训练两类模型来实现的。此外，本文提出了一个数据集佩戴面罩检测（WMD），包括7804真实的图像。数据集有26403戴口罩，覆盖多个场景，可在\"https://github.com/BingshuCV/WMD获得。“对数据集进行的实验表明，该方法对简单场景的总体精度为97.32％，对复杂场景的总体精度为91.13％，性能优于所比较的方法。
