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http://dx.doi.org/10.33778/kcsa.2022.22.2.021

Design of Face with Mask Detection System in Thermal Images Using Deep Learning  

Yong Joong Kim (한국폴리텍대학 원주캠퍼스/의료공학과)
Byung Sang Choi (한국폴리텍대학 원주캠퍼스/의료공학과)
Ki Seop Lee ((주)엘에스엘시스템즈)
Kyung Kwon Jung (동신대학교/사물인터넷학과)
Publication Information
Convergence Security Journal / v.22, no.2, 2022 , pp. 21-26 More about this Journal
Abstract
Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.
Keywords
face mask detection; thermal image; deep learning; MTCNN;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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