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2019冠状病毒病中的口罩检测：一项策略性综述

Face mask detection in COVID-19: a strategic review.

作者信息

Jindal Neeru, Singh Harpreet, Rana Prashant Singh

机构信息

Research Scholar, ECED, Thapar Institute of Engineering and Technology, Patiala, Punjab India.

Faculty, ECED, Thapar Institute of Engineering and Technology, Patiala, Punjab India.

出版信息

Multimed Tools Appl. 2022;81(28):40013-40042. doi: 10.1007/s11042-022-12999-6. Epub 2022 May 5.

DOI:10.1007/s11042-022-12999-6
PMID:35528282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9069221/
Abstract

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.

摘要

随着2019年冠状病毒病的爆发，生活似乎陷入了停滞。为了抗击病毒传播，世界卫生组织（WHO）宣布佩戴口罩是限制病毒传播的必要方式。然而，手动确保公共场所的人们是否佩戴口罩是一项繁琐的任务。监测人们佩戴口罩的紧迫性使得有必要构建一个自动系统。目前，使用机器学习和深度学习的不同方法可以有效利用。本文回顾了此类模型的所有基本要求。广泛讨论了所提出模型的需求和结构概述，随后对各种可用技术及其各自的比较性能分析进行了全面研究。此外，深入分析了每种方法的优缺点。随后，提到了多个数据集的来源。还讨论了实现所需的几种软件。并针对该系统的各种用例、局限性和观察结果进行了讨论，以及本文的结论和未来研究的几个方向。

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