밀폐공간 내 감염병 위험도 모니터링을 위한 열화상 온도 스크리닝 시스템 설계 및 구현에 대한 연구

Abstract

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms ofrespiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constantmonitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera moduleand the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environmentwas calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithmand the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module andthe linear regression correction module. To measure the performance of the proposed model, an experimental environment consistingof a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.