Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (10): 13-26.DOI: 10.3778/j.issn.1002-8331.2110-0396

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Research on Face Mask Wearing Detection

WANG Xinran, TIAN Qichuan, ZHANG Dong   

  1. 1.School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    2.Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 100044, China
  • Online:2022-05-15 Published:2022-05-15



  1. 1.北京建筑大学 电气与信息工程学院,北京 100044
    2.建筑大数据智能处理方法研究北京市重点实验室,北京 100044

Abstract: Face mask wearing detection is an emerging research topic that has developed rapidly in the past two years in the context of the global COVID-19 epidemic. Under regular epidemic situation, wearing masks is an important means of effective epidemic prevention, therefore it is essential to remind and check people whether to wear masks in public places. Using artificial intelligence to complete mask wearing detection can achieve the purpose of real-time supervision, save human resources and effectively avoid mistakes, missed detection and other problems. The models and relevant algorithms used in current mask wearing detection research are reviewed. Firstly, the task and application background of mask wearing detection are described. Then, the detection algorithms based on deep neural networks and object detection models are summarized and  analyzed, the advantages and disadvantages, improvement methods and application scenarios of different research schemes are discussed. Secondly, common related data sets are introduced, and the detection performance of each algorithm is compared. Finally, the existing problems and the direction of future development are discussed and prospected.

Key words: mask wearing detection, deep neural networks, object detection

摘要: 人脸口罩佩戴检测是近两年在全球新冠疫情背景下快速发展的一个新兴研究课题。疫情常态下,佩戴口罩是有效防疫的重要手段,因此公共场所下对人员是否佩戴口罩的检查与提醒必不可少。利用人工智能完成口罩佩戴检测工作可以达到实时监督的目的,节省人力资源,有效避免误检、漏检等问题。对当前口罩佩戴检测研究所使用的网络模型和相关算法进行了详细梳理。针对口罩佩戴检测任务及其应用背景进行了简要说明;重点总结和分析了基于深度神经网络和基于目标检测模型两种思路的检测算法,主要讨论了不同研究方案的优缺点、改进方法和适用场景;介绍了常用的相关数据集,对比展现了各算法检测性能;对仍然存在的问题以及未来发展的方向进行了探讨和展望。

关键词: 口罩佩戴检测, 深度神经网络, 目标检测