Paper Title
Prevent the COVID-19 Spreading using Face Mask Detection in CNN

Abstract
Due to the global COVID-19 pandemic, computer vision education has received increasing attention to improve public health services. At the time of death, because a pair of classification and detection are used under the video image, detecting small objects in image processing is a more difficult task. Deep neural network detection has shown useful object detection with excellent performance, namely mask detection. For inevitable natural diseases, it is a unique topic because of the benefits it brings to people. Added mask detection that works with YOLOv3 and can measure real-time performance through a powerful GPU. Then, we have some people who use or do not use masks to train people with mask images but no mask images. The results of detection, location and detection experiments show that the average loss after training for 4000 epochs is 0.0730. After training 4000 epochs, the MAP value is 0.96.