Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector

被引：5

作者：

Aadithya, V ^{[1
]}

Balakumar, S. ^{[1
]}

Bavishprasath, M. ^{[1
]}

Raghul, M. ^{[1
]}

Malathi, P. ^{[1
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India

来源：

SUSTAINABLE COMMUNICATION NETWORKS AND APPLICATION, ICSCN 2021 | 2022年 / 93卷

关键词：

YOLOv3; MobileNetv2; Face mask; Artificial Neural Network; Image processing;

D O I：

10.1007/978-981-16-6605-6_61

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 this Covid-19, face masks are used to avoid cross-contamination as part of an infection protection strategy. Wearing a face mask can help avoid infection by preventing individuals from coming into contact with pathogens. When someone coughs, speaks, or sneezes, there is a chance that the infection will spread into the air and affect those nearby. So to prevent the rate of spreading, face masks are highly mandatory. Tracking every individual manually is an expensive task; therefore, we save a lot of time, cost and effort by automating this process. This proposed automation can be done using Artificial Neural Networks. YOLOv3 and MobileNetv2 are popular architectures used in different object detection applications. Hence, paper compares the above architectures by their performance, accuracy from outputs of both under different scenarios.

引用

页码：801 / 809

页数：9

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