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http://dx.doi.org/10.9718/JBER.2022.43.3.131

The Study for Type of Mask Wearing Dataset for Deep learning and Detection Model  

Hwang, Ho Seong (Department of Medical Artificial Intelligent, Eul-Ji University)
Kim, Dong heon (Synapseimaging)
Kim, Ho Chul (Department of Radiological Science, Eul-Ji University)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.3, 2022 , pp. 131-135 More about this Journal
Abstract
Due to COVID-19, Correct method of wearing mask is important to prevent COVID-19 and the other respiratory tract infections. And the deep learning technology in the image processing has been developed. The purpose of this study is to create the type of mask wearing dataset for deep learning models and select the deep learning model to detect the wearing mask correctly. The Image dataset is the 2,296 images acquired using a web crawler. Deep learning classification models provided by tensorflow are used to validate the dataset. And Object detection deep learning model YOLOs are used to select the detection deep learning model to detect the wearing mask correctly. In this process, this paper proposes to validate the type of mask wearing datasets and YOLOv5 is the effective model to detect the type of mask wearing. The experimental results show that reliable dataset is acquired and the YOLOv5 model effectively recognize type of mask wearing.
Keywords
Mask detection; Deep learning; Object classification; Object detection;
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