Chi-Yen Li｜PhD student, Department of Business Administration, National Central University｜rickyyenli@gmail.com
Su-Chi Fuh｜Master student, Department of Computer Science & Information Engineering, National Taipei University of Technology｜es891010@gmail.com
Chen-Jie Lin｜ Master student, Department of Computer Science, National Taipei University of Education｜ckdcshadow@gmail.com
▌Abstract
The extremely high transmission rate of the COVID-19 has made the supply of medical resources in countries around the world in short supply. In view of the fact that wearing masks is currently an effective method of epidemic prevention, and the current face detection models are not effective for masked faces that cover half of their faces, and pedestrians who have not worn masks in the correct way are occasionally visible. It may spread the epidemic.
This research will establish a face data set with three kinds of annotations, and combine a variety of deep learning convolutional neural network architectures and methods to design a face detection model that can quickly train and detect wearing a mask, not wearing a mask, and wearing a mask incorrectly faces. Hope to contribute to the epidemic prevention and control of the epidemic.
We use an adaptive algorithm to adjust the image size to reduce unnecessary operations, and modify the CIOU_LOSS error function to speed up the operation. Experiments have confirmed that our algorithm saves 70% of the time compared to YOLO v5m with the same accuracy.
Keywords: COVID-19, Convolutional Neural Network, Face Detection, Masked