@inproceedings{2bc44b2034264973ac6d38760ba2935b,
title = "FCOSMask: Fully Convolutional One-Stage Face MaskWearing Detection Based on MobileNetV3",
abstract = "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..",
keywords = "Anchor free, Face mask wearing detection, Lightweight network, Multi-level prediction",
author = "Yang Yu and Jie Lu and Chao Huang and Bo Xiao",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computing Machinery. All rights reserved.; 5th International Conference on Computer Science and Application Engineering, CSAE 2021 ; Conference date: 19-10-2021 Through 21-10-2021",
year = "2021",
month = oct,
day = "19",
doi = "10.1145/3487075.3487078",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
editor = "Ali Emrouznejad",
booktitle = "CSAE 2021 - Proceedings of the 5th International Conference on Computer Science and Application Engineering",
address = "美国",
}