基于轻量级分类网络的印刷电路板焊点质量检测

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Zhicong Zhang, Wenyu Zhang, Donglin Zhu, Yi Xu, Changjun Zhou
{"title":"基于轻量级分类网络的印刷电路板焊点质量检测","authors":"Zhicong Zhang,&nbsp;Wenyu Zhang,&nbsp;Donglin Zhu,&nbsp;Yi Xu,&nbsp;Changjun Zhou","doi":"10.1049/csy2.12101","DOIUrl":null,"url":null,"abstract":"<p>Solder joint quality inspection is a crucial step in the qualification inspection of printed circuit board (PCB) components, and efficient and accurate inspection methods will greatly improve its production efficiency. In this paper, we propose a PCB solder joint quality detection algorithm based on a lightweight classification network. First, the Select Joint segmentation method was used to obtain the solder joint information, and colour space conversion was used to locate the solder joint. The mask method, contour detection, and box line method were combined to complete the extraction of solder joint information. Then, by combining the respective characteristics of convolutional neural network and Transformer and introducing Cross-covariance attention to reduce the computational complexity and resource consumption of the model and evenly distribute the global view mutual information in the whole training process, a new lightweight network model MobileXT is proposed to complete defect classification. Only 16.4% of the Vision Transformer computing resources used in this model can achieve an average accuracy improvement of 31%. Additionally, the network is trained and validated using a dataset of 1804 solder joint images constructed from 93 PCB images and two external datasets to evaluate MobileXT performance. The proposed method achieves more efficient localization of the solder joint information and more accurate classification of weld joint defects, and the lightweight model design is more appropriate for industrial edge device deployments.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12101","citationCount":"0","resultStr":"{\"title\":\"Printed circuit board solder joint quality inspection based on lightweight classification network\",\"authors\":\"Zhicong Zhang,&nbsp;Wenyu Zhang,&nbsp;Donglin Zhu,&nbsp;Yi Xu,&nbsp;Changjun Zhou\",\"doi\":\"10.1049/csy2.12101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Solder joint quality inspection is a crucial step in the qualification inspection of printed circuit board (PCB) components, and efficient and accurate inspection methods will greatly improve its production efficiency. In this paper, we propose a PCB solder joint quality detection algorithm based on a lightweight classification network. First, the Select Joint segmentation method was used to obtain the solder joint information, and colour space conversion was used to locate the solder joint. The mask method, contour detection, and box line method were combined to complete the extraction of solder joint information. Then, by combining the respective characteristics of convolutional neural network and Transformer and introducing Cross-covariance attention to reduce the computational complexity and resource consumption of the model and evenly distribute the global view mutual information in the whole training process, a new lightweight network model MobileXT is proposed to complete defect classification. Only 16.4% of the Vision Transformer computing resources used in this model can achieve an average accuracy improvement of 31%. Additionally, the network is trained and validated using a dataset of 1804 solder joint images constructed from 93 PCB images and two external datasets to evaluate MobileXT performance. The proposed method achieves more efficient localization of the solder joint information and more accurate classification of weld joint defects, and the lightweight model design is more appropriate for industrial edge device deployments.</p>\",\"PeriodicalId\":34110,\"journal\":{\"name\":\"IET Cybersystems and Robotics\",\"volume\":\"5 4\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12101\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Cybersystems and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/csy2.12101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cybersystems and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/csy2.12101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

焊点质量检验是印刷电路板(PCB)元器件合格检验的关键环节,高效、准确的检验方法将大大提高其生产效率。本文提出了一种基于轻量级分类网络的PCB焊点质量检测算法。首先,采用选择焊点分割法获取焊点信息,并采用色彩空间变换对焊点进行定位;结合掩模法、轮廓检测法和盒线法完成焊点信息的提取。然后,结合卷积神经网络和Transformer各自的特点,引入交叉协方差关注,降低模型的计算复杂度和资源消耗,并在整个训练过程中均匀分布全局视图互信息,提出一种新的轻量级网络模型MobileXT来完成缺陷分类。在该模型中使用的Vision Transformer计算资源中,只有16.4%的资源可以实现31%的平均精度提高。此外,使用由93张PCB图像和两个外部数据集组成的1804张焊点图像数据集对网络进行训练和验证,以评估MobileXT的性能。该方法实现了更高效的焊点信息定位和更准确的焊缝缺陷分类,且轻量化模型设计更适合工业边缘器件部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Printed circuit board solder joint quality inspection based on lightweight classification network

Printed circuit board solder joint quality inspection based on lightweight classification network

Solder joint quality inspection is a crucial step in the qualification inspection of printed circuit board (PCB) components, and efficient and accurate inspection methods will greatly improve its production efficiency. In this paper, we propose a PCB solder joint quality detection algorithm based on a lightweight classification network. First, the Select Joint segmentation method was used to obtain the solder joint information, and colour space conversion was used to locate the solder joint. The mask method, contour detection, and box line method were combined to complete the extraction of solder joint information. Then, by combining the respective characteristics of convolutional neural network and Transformer and introducing Cross-covariance attention to reduce the computational complexity and resource consumption of the model and evenly distribute the global view mutual information in the whole training process, a new lightweight network model MobileXT is proposed to complete defect classification. Only 16.4% of the Vision Transformer computing resources used in this model can achieve an average accuracy improvement of 31%. Additionally, the network is trained and validated using a dataset of 1804 solder joint images constructed from 93 PCB images and two external datasets to evaluate MobileXT performance. The proposed method achieves more efficient localization of the solder joint information and more accurate classification of weld joint defects, and the lightweight model design is more appropriate for industrial edge device deployments.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信