Mask wearing detection based on improved YOLOv7

FU Hui-chen; GAO Jun-wei; CHE Lu-yang

doi:10.37188/CJLCD.2022-0371

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Mask wearing detection based on improved YOLOv7

Image Processing | 更新时间：2023-08-10

- Mask wearing detection based on improved YOLOv7
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 8, Pages: 1139-1147(2023)
- 作者机构：
  
  1.青岛大学自动化学院，山东青岛 266071
  2.山东省工业控制技术重点实验室，山东青岛 266071
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Shandong Province(ZR2019MF063)
- DOI：10.37188/CJLCD.2022-0371
  CLC：
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FU Hui-chen, GAO Jun-wei, CHE Lu-yang. Mask wearing detection based on improved YOLOv7. [J]. Chinese Journal of Liquid Crystals and Displays 38(8):1139-1147(2023)
DOI：

FU Hui-chen, GAO Jun-wei, CHE Lu-yang. Mask wearing detection based on improved YOLOv7. [J]. Chinese Journal of Liquid Crystals and Displays 38(8):1139-1147(2023) DOI： 10.37188/CJLCD.2022-0371.

摘要

佩戴好口罩是居民预防新冠和配合国家疫情防控的有效方式。针对口罩佩戴是否正确、拍摄角度不同以及被遮挡等问题，提出了一种改进的YOLOv7算法。该算法以YOLOv7为基础，在网络的Head区引入卷积注意力机制，使得特征网络在对口罩区域的处理中更具有针对性，从而增强特征网络对口罩区域的学习能力；对Backbone区结构进行优化，对ConvNeXt网络结构进行改进，并引入网络中代替部分卷积，提高模型的检测精度和鲁棒性，增强预测精确度的同时不会引入大量额外的计算。对Head层的空间金字塔池化进行改进，提高了训练速度并且加快模型收敛。实验结果表明，在复杂及遮挡的情况下，改进后的YOLOv7的损失函数大幅下降，在测试集上的mAP为93.8%，相比于原始YOLOv7算法提高了3.6%。各个类别的检测精度均有提升，没佩戴口罩、正确佩戴口罩、不正确佩戴口罩类别的精度分别提升6.8%、2.1%、1.7%。本文算法的错检情况明显减少，泛化能力有显著提升。

Abstract

Wearing masks is an effective way for preventing COVID-19 and cooperating with the national epidemic prevention and control. An improved YOLOv7 algorithm is proposed to solve the problems such as whether masks are correctly worn， different shooting angles and being blocked. Based on YOLOv7， the convolutional attention mechanism is introduced into the Head region of the network to make the feature network more targeted in the processing of the mask region， thus enhancing the learning ability of the feature network to the mask region. The structure of Backbone area is optimized， the ConvNeXt network structure is improved， and partial convolution is introduced into the network instead， which improves the detection accuracy and robustness of the model and enhances the accuracy of prediction without introducing a large number of additional calculations. The space pyramid pool of the Head layer is improved to improve the training speed and accelerate the model convergence. Experiments show that in the case of complexity and occlusion， the loss function of the improved YOLOv7 decreases significantly， and the mAP on the test set is 93.8%， which is 3.6% higher than that of the original YOLOv7 algorithm.The accuracy of each category is improved， and the accuracy of no mask， correct mask and incorrect mask are increased by 6.8%， 2.1% and 1.7%， respectively. The cases of error detection are significantly reduced， and the generalization ability is significantly improved.

关键词

图像处理目标检测YOLOv7算法卷积注意力机制空间金字塔池化

Keywords

image processingobject detectionYOLOv7 algorithmconvolutional attention mechanismspace pyramid pooling

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