Automatic Mask Detection at Public Places

Abstract

COVID-19's fast spread has resulted in tens of millions of individuals becoming infected around the world. Because there is no specific cure for COVID-19, wearing masks has proven an effective technique of preventing transmission and is mandatory in most public places, resulting in an increase in demand for automatic real-time mask detection systems to replace manual reminders. However, there are just a few investigations on the detection of face masks. The performance of mask detectors must be improved urgently. However, there have been few studies on the detection of face masks. Mask detector performance must be improved immediately. The Properly Wearing Masked Face Detection Dataset (PWMFD) was proposed in this paper, and it includes 9205 photos of mask-wearing samples divided into three groups. Squeeze and Excitation (SE)-YOLOv3, a mask detector with roughly balanced effectiveness and efficiency, was also proposed. by incorporating the SE block into Darknet53, I was able to incorporate the attention mechanism and obtain the relationships between channels, allowing the network to focus more on the relevant feature. To increase the stability of bounding box regression, I used GIoUloss, which can better express the spatial difference between predicted and ground truth boxes. The significant foreground-background class imbalance was solved via focal loss. In addition, I used image augmentation techniques to boost the model's robustness on the challenge. In comparison to YOLOv3, SE-YOLOv3 outperformed YOLOv3 and other state-of-the-art detectors on PWMFD, achieving a higher 8.6 percent mAP while maintaining a comparable detection speed.