Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

被引：65

作者：

Kong, Xiangjie ^{[1
]}

Wang, Kailai ^{[2
]}

Wang, Shupeng ^{[3
]}

Wang, Xiaojie ^{[4
]}

Jiang, Xin ^{[5
]}

Guo, Yi ^{[5
]}

Shen, Guojiang ^{[1
]}

Chen, Xin ^{[2
]}

Ni, Qichao ^{[2
]}

机构：

[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China

[2] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China

[3] Chinese Acad Sci, Inst Informat Engn, Beijing 100864, Peoples R China

[4] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China

[5] Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Guangzhou 510632, Peoples R China

来源：

IEEE INTERNET OF THINGS JOURNAL | 2021年 / 8卷 / 21期

基金：

中国国家自然科学基金;

关键词：

Streaming media; Edge computing; Real-time systems; COVID-19; Monitoring; Deep learning; Image restoration; Coronavirus disease 2019 (COVID-19); deep learning; edge computing; Internet of Things (IoT); mask identification; public health prevention; MEDICAL THINGS; INTERNET; NETWORK;

D O I：

10.1109/JIOT.2021.3051844

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

引用

页码：15929 / 15938

页数：10