RESUMO
With 6.93M confirmed cases of COVID-19 worldwide, making individuals aware of their sanitary health and ongoing pandemic remains the only way to prevent the spread of this virus. Wearing masks is an important step in this prevention. Hence, there is a need for monitoring if people are wearing masks or not. Closed circuit television (CCTV) cameras endowed with computer vision function by embedded systems, have become popular in a wide range of applications, and can be used in this case for real time monitoring of people wearing masks or not. In this paper, we propose to model this task of monitoring as a special case of object detection. However, real-time scene parsing through object detection running on edge devices is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we used a few popular object detection algorithms such as YOLOv3, YOLOv3Tiny, SSD and Faster R-CNN and evaluated them on Moxa3K benchmark dataset. The results obtained from these evaluations help us to determine methods that are more efficient, faster, and thus are more suitable for real-time object detection specialized for this task.
