基于协同注意力的小样本的手机屏幕缺陷分割

许国良; 毛骄

doi:10.11999/JEIT210054

基于协同注意力的小样本的手机屏幕缺陷分割

doi: 10.11999/JEIT210054

许国良^2, ,,
毛骄^{1, 2}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学电子信息与网络工程研究院重庆 400065

基金项目: 重庆市技术创新与应用示范(产业类重点研发)项目(cstc2018jszx-cyzdX0124)

详细信息

作者简介:
许国良：男，1973年生，教授，硕士生导师，研究方向为光电传感与检测、通信网络设计与规划、大数据分析挖掘

毛骄：女，1997年生，硕士生，研究方向为深度学习、图像处理

通讯作者:
许国良　xugl@cqupt.edu.cn

中图分类号: TN911.73; TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-18
- 修回日期: 2021-05-28
- 网络出版日期: 2021-08-26
- 刊出日期: 2022-04-18

Few-Shot Segmentation on Mobile Phone Screen Defect Based on Co-Attention

XU Guoliang^{2
, ,},
MAO Jiao^{1, 2}

1.
Institute of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The Chongqing Technology Innovation and Application Demonstration Special Project -- Key Industrial R&D Projects (cstc2018jszx-cyzdX0124)

摘要

摘要: 在手机屏幕工业化生产过程中，缺陷检测的好坏直接影响手机屏幕的合格率。少量的缺陷样本不足以完成数据驱动的分割网络的训练，因此如何利用少量的缺陷图像完成缺陷分割成为关键问题。该文针对此问题提出一种基于协同注意力的小样本手机屏幕缺陷分割网络(Co-ASNet)。该网络使用交叉注意力块在特征提取时获取更加丰富的上下文缺陷特征信息，同时引入了协同注意力的方式来加强支持图像与查询图像相同缺陷目标之间的特征信息交互，增强缺陷特征表示，另外，使用了改进的联合损失函数来完成网络的训练。该文采用手机屏幕缺陷数据集进行实验，实验结果表明，Co-ASNet能够使用少量的缺陷样本完成良好的缺陷分割效果。
- 手机屏幕缺陷 /
- 小样本分割 /
- 协同注意力 /
- 交叉注意力
Abstract: In the commercial process of mobile phone screens, the quality of defect detection affects directly the qualified rate of mobile phone screens. A few defect samples are not enough to complete the training of data-driven segmentation networks, so how to use a few defect samples to complete the defect segmentation is a key problem. In view of this problem, a Co-Attention Segmentation Network (Co-ASNet) is proposed. This network uses Criss-cross attention blocks to capture contextual defect feature information during feature extraction. At the same time, the Co-attention method is applied to enhance the defect feature information interaction between the same defect target in the support image and query image, and then the defect feature representation is reinforced. Also, the improved joint loss function is used to complete the network training. The experimental results show that Co-ASNet can use a few defect samples to achieve an excellent effect of defect segmentation.
- Mobile phone screen defects /
- Few-shot segmentation /
- Co-attention /
- Criss-cross attention

HTML全文

图 1 基于协同注意力的小样本手机屏幕缺陷分割网络的网络架构图

下载: 全尺寸图片幻灯片

图 2 特征提取模块示意图

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图 3 交叉注意力块示意图

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图 4 特征增强模块架构图

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图 5 编码-解码过程示意图

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图 6 不同分割网络对手机屏幕缺陷图像的分割效果可视化

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图 7 1-shot下的手机屏幕缺陷图像的分割效果可视化

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图 8 5-shot下的手机屏幕缺陷图像的分割效果可视化

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表 1 手机屏幕缺陷图像数据集

类别	点	线	面
缺陷图像
掩膜图像

下载: 导出CSV

表 2 不同分割网络模型在手机屏幕缺陷数据集的性能比较

模型	PA	MPA	MIoU	FWMIoU
U-net	0.9610	0.4635	0.4074	0.9334
SG-One(1-shot)	0.9658	0.5392	0.4647	0.9412
SG-One(5-shot)	0.9669	0.5199	0.4622	0.9432
Co-ASNet(1-shot)	0.9712	0.6435	0.5588	0.9489
Co-ASNet(5-shot)	0.9709	0.6711	0.5771	0.9482

下载: 导出CSV

表 3 在手机屏幕缺陷图像数据集上的分割结果(MIoU)

模型	1-shot	5-shot
SG-One	0.4647	0.4622
SG-One + cc-block	0.5244	0.5592
SG-One + co-a ($L{\rm{ }} = {l_{{\rm{query}}}}$)	0.4563	0.4584
SG-One + co-a	0.5476	0.5701
Co-ASNet($L{\rm{ }} = {l_{{\rm{query}}}}$)	0.4988	0.5380
Co-ASNet	0.5588	0.5771

下载: 导出CSV

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