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http://dx.doi.org/10.36498/kbigdt.2022.7.1.63

Embedded Mask Recognition System using YOLOv5  

Ga-Won Yu (동의대학교 대학원 컴퓨터소프트웨어공학과 부산IT융합부품연구소)
Eun-Sung Choi (동의대학교 대학원 인공지능학과 부산IT융합부품연구소)
Young-Jin Kang (동의대학교 인공지능그랜드ICT연구센터)
Jeon, Young Jun (동의대학교 부산IT융합부품연구소)
Jeong, Seok Chan (동의대학교 e비즈니스학과 인공지능그랜드ICT연구센터 부산IT융합부품연구소)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 63-73 More about this Journal
Abstract
COVID-19 has continued from 2020 to the present, and many social changes have occurred. Wearing a mask has become mandatory, and if you do not wear a mask, you cannot use public facilities or restaurants. For this reason, most public facility entrances are equipped with a mask recognition system to check whether a mask is worn. However, it is unclear whether people who cover their mouths with a scarf or who do not wear a mask properly can be identified. In this study, we proposed an embedded mask recognition system using YOLOv5. Unlike the existing mask recognition system, it was able to distinguish not only whether a mask was worn, but also whether a mask was worn in various exceptional situations, such as a person with a scarf or a person covering their mouth with their hands, and showed excellent performance when mounted on the Nvida Jetson Nano Board.
Keywords
AI; Deep Learning; YOLOv5; Mask Recognition; Embedded system;
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