Abstract: Wearing a mask is recognized by global medical experts as one of the most effective ways to prevent COVID-19 infection. The vision-based intelligent mask detection plays an important role in urging people to wear masks in public. However, compared with general object detection, there are currently few studies focusing on  mask detection. To solve the problem, an optimized multi-scale awareness network, called PyramidMask, is proposed for  mask detection. Firstly, PyramidMask obtains the multi-layer features of the image from different scales of the backbone. Secondly, the scale-awareness branches are designed to perform independent predictions of different layers of high-density candidate boxes. Finally, the multi-scale faces with masks in an image is accurately detected in an end-to-end manner. In addition, in order to improve the robustness of PyramidMask under complex scenes, the training samples are augmented by image stitching in the training stage. The experimental results show that PyramidMask outperforms the state-of-the-art methods on the public mask detection dataset. Compared with the benchmark, PyramidMask improves 5.4% and 12.5% in the recall of detection with and without masks, and 6.0% and 4.1% in the precision of detection with and without masks.

Key words: COVID-19, face mask detection, computer vision, deep learning