改进YOLOv4-tiny的疫情协同口罩佩戴检测方法

doi:10.3778/j.issn.1002-8331.2204-0473

摘要/Abstract

摘要： 口罩的佩戴对于疫情防控起着极其重要的作用，针对大规模人群下口罩佩戴检测实时性欠佳、难以部署的问题，提出了一种改进YOLOv4-tiny的疫情协同口罩佩戴检测方法。该算法以YOLOv4-tiny为基础，用两个Resblock-D模块替代CSP模块，降低特征提取网络复杂度，提升检测速度；引入SPP，增加了网络的感受野，使网络满足任意尺寸的影像输入，并增强算法的鲁棒性；引入两层CA注意力机制，提高算法的利用率以保证检测精确度。通过实验检测结果表明，相较于原始YOLOv4-tiny，所提网络mAP提升了0.5个百分点，达到94.0%，检测速度提升了3.96?FPS。在保证检测速度有少量提升的基础上有效提高了检测速度，综合性能得到提升。

关键词: 目标检测, YOLOv4-tiny, 口罩检测, 注意力机制（CA）, 空间金字塔池化, Resblock-D

Abstract: The wearing of masks plays an extremely important role in epidemic control. In view of the problem of poor real-time detection of mask wearing in large-scale crowds and difficult to deploy, an modified YOLOv4-tiny epidemic collaborative mask wearing detection method is put forward. The algorithm is based on YOLOv4-tiny, and replaces the CSP modules with Resblock-D modules, which reduces the complexity of the feature extraction network and improves the detection speed; the introduction of SPP increases the receptive field of the network, so that the network can meet the image input of any size and enhance the robustness of the algorithm; it introduces a two-layer CA attention mechanism to improve the utilization of the algorithm to ensure the detection accuracy. Compared with the traditional algorithm YOLOv4-tiny, the experimental rasults reveals that the proposed network mAP is improved by 0.5 percentage points to 94.0%, and the detection speed is improved by 3.96 FPS. On the basis of making ensure a small promotion in detection accuracy, the detection accuracy is effectively increased, and the overall performance is improved.

Key words: target detection, YOLOv4-tiny, mask testing, coordinate attention（CA）, spatial pyramid pooling, Resblock-D

程浩然, 王薪陶, 李俊燃, 郭子怡, 刘维. 改进YOLOv4-tiny的疫情协同口罩佩戴检测方法[J]. 计算机工程与应用, 2023, 59(20): 208-218.

CHENG Haoran, WANG Xintao, LI Junran, GUO Ziyi, LIU Wei. Improved YOLOv4-tiny Epidemic Collaborative Mask Wearing Detection Method[J]. Computer Engineering and Applications, 2023, 59(20): 208-218.

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