论文标题
视网膜上：一个单阶段面膜探测器，用于协助控制Covid-19大流行
RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic
论文作者
论文摘要
2019年冠状病毒对世界产生了重大影响。防止人们感染的一种有效策略是在公共场所戴口罩。某些公共服务提供商只有在适当戴口罩的情况下才能使用其服务。但是，只有少数关于自动面膜检测的研究。在本文中，我们提出了Retinafacemask，这是第一个高性能的单阶段面膜探测器。首先，为了解决现有研究没有区分正确和不正确的面具戴州的问题，我们建立了一个包含这些注释的新数据集。其次，我们提出了一个上下文注意模块，以专注于学习与戴面膜戴州相关的歧视特征。第三，我们从面部检测任务中转移了知识，灵感来自于人类如何通过从类似任务中学习来提高其能力。消融研究表明该模型的优势。公众和新数据集的实验发现证明了我们模型的最新性能。
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.
