FCOSMask:基于MobileNetV3的全卷积单阶段口罩佩戴检测

Yang Yu, Jie Lu, Chao Huang, Bo Xiao
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引用次数: 0

摘要

在公共场合正确佩戴口罩是应对2019冠状病毒病(COVID-19)的主要自我预防方法之一。本文提出了一种基于轻量化网络的全卷积一级口罩佩戴检测器FCOSMask,用于突发疫情防控和长期疫情防控工作。采用MobileNetV3作为骨干网,减少计算开销。因此,在无锚方法中避免了与锚盒相关的复杂计算,并选择CIoU (Complete Intersection over Union)损失作为边界盒回归损失函数,加快了模型的收敛速度。实验表明,与其他基于锚点的方法相比,FCOSMask在自建数据集上的检测速度提高了3 ~ 4倍左右,平均精度(mAP)达到92.4%,满足了大多数公共区域口罩佩戴检测任务的准确性和实时性要求。最后,开发了一个基于web的支持公共疫情防控管理的口罩佩戴系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FCOSMask: Fully Convolutional One-Stage Face Mask Wearing Detection Based on MobileNetV3
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.
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