FCOSMask: Fully Convolutional One-Stage Face MaskWearing Detection Based on MobileNetV3
5th International Conference on Computer Science and Application Engineering, CSAE 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1599479
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.. © 2021 Association for Computing Machinery. All rights reserved.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th International Conference on Computer Science and Application Engineering, CSAE 2021
Year:
2021
Document Type:
Article
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