Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model.
翻译：2019年科罗纳病毒对世界产生了重大影响。 预防人们感染的有效战略之一是在公共场所戴面罩。 某些公共服务提供者要求客户只有在适当佩戴面罩时才使用其服务。 然而,关于自动戴面罩检测的研究只有少数。 在本文中,我们提议了首个高性能单级面罩检测器Retina FaceMask。 首先,为了解决现有研究没有区分戴面罩状态正确和不正确的问题,我们建立了一个包含这些说明的新数据集。 其次,我们提议了一个背景关注模块,重点关注学习与戴面罩状态有关的受歧视特征。 第三,我们传授了通过从类似任务中学习人类如何提高自身能力的面对面检测任务知识。 吸收研究表明了拟议模型的优势。 公众实验结果和新数据集显示了我们模型的最新表现。
相关内容
微信扫码咨询专知VIP会员