Development of Mask Classification and Wearing Detection Program with Image Processing Technology
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Abstract
Development of mask classification and wearing detection program with image processing technology which is purpose to classify and detect wearing masks. There are three steps 1) data set preparation 2) model training and 3) mask classification and wearing. Our research collected datasets with 4 classes; class 1: non-wearing mask, class 2: surgical mask, class 3: cloth mask, and class 4: N95 mask. Our mask classification and wearing detection experiment, two algorithms which are YOLOv4 on Tensorflow and YOLOv5 on Pytorch are compared with 2,311 datasets. We found that the average of accuracy (mAP) of the YOLOv5 (0.9943) is better than the YOLOv4 (0.9057) while the YOLOv5 can classify the cloth mask with the maximum F1-score at 0.9995. Twelve persons are set in the real situation with webcam. The result shown that the YOLOv5 model can correctly detect the 11 persons (0.91%).
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