{"title":"FCOSMask: Fully Convolutional One-Stage Face Mask Wearing Detection Based on MobileNetV3","authors":"Yang Yu, Jie Lu, Chao Huang, Bo Xiao","doi":"10.1145/3487075.3487078","DOIUrl":null,"url":null,"abstract":"Wearing masks correctly in public is one major self-prevention method against the worldwide Coronavirus disease 2019 (COVID-19). This paper proposes FCOSMask, a fully convolutional one-stage face mask wearing detector based on the lightweight network, for emergency epidemic control and long-term epidemic prevention work. MobileNetV3 is applied as the backbone network to reduce computational overhead. Thus, complex calculation related to anchor boxes is avoided in the anchor-free method, and Complete Intersection over Union (CIoU) loss is selected as the bounding box regression loss function to speed up model convergence. Experiments show that compared to other anchor-based methods, detection speed of FCOSMask is improved around 3 to 4 times on self-established datasets and mean average precision (mAP) achieves 92.4%, which meets the accuracy and real-time requirements of the face mask wearing detection task in most public areas. Finally, a Web-based face mask wearing system is developed that can support public epidemic prevention and control management.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3487075.3487078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Wearing masks correctly in public is one major self-prevention method against the worldwide Coronavirus disease 2019 (COVID-19). This paper proposes FCOSMask, a fully convolutional one-stage face mask wearing detector based on the lightweight network, for emergency epidemic control and long-term epidemic prevention work. MobileNetV3 is applied as the backbone network to reduce computational overhead. Thus, complex calculation related to anchor boxes is avoided in the anchor-free method, and Complete Intersection over Union (CIoU) loss is selected as the bounding box regression loss function to speed up model convergence. Experiments show that compared to other anchor-based methods, detection speed of FCOSMask is improved around 3 to 4 times on self-established datasets and mean average precision (mAP) achieves 92.4%, which meets the accuracy and real-time requirements of the face mask wearing detection task in most public areas. Finally, a Web-based face mask wearing system is developed that can support public epidemic prevention and control management.