A Fine-Grained Detector of Face Mask Wearing Status Based on Improved YOLOX.

The fast outbreak of COVID-19 and rapid prolif-eration of its variants have continued to pose a huge challenge to people around the world. Wearing medical masks properly in public and private settings can protect people from COVID-19 which brings a growing demand for automatic detection services of face mask wearing conditions. In this paper, we propose a fine-grained detector called ECA YOLOX-S to identify the wearing status of face masks. Efficient Channel Attention (ECA) is introduced into YOLOX-S to reach a trade-off between effectiveness and efficiency. To demonstrate the performance of our proposed method, a Fine-grained Face Mask (FineFM) dataset is created which covers four classes of mask wearing status. The proposed FineFM dataset has 16,955 annotated images and covers multiple realistic scenarios, which is available at https://github.com/HongliXiao/FineFM . To our knowledge, it has the largest number of improper mask wearing images among all similar datasets for realistic scenes. Experiments conducted on the FineFM dataset demonstrate that ECA YOLOX-S achieves an overall mAP@.50:95 of 86.80% for moderate scenes and an overall mAP@.50:95 of 73.20% for complex scenes, outper-forming its benchmark model. Moreover, experiments conducted on other realistic and simulated datasets indicate the proposed detector has advantages over other methods.