Cargando…

Using ISU-GAN for unsupervised small sample defect detection

Surface defect detection is a vital process in industrial production and a significant research direction in computer vision. Although today’s deep learning defect detection methods based on computer vision can achieve high detection accuracy, they are mainly based on supervised learning. They requi...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Yijing, Zhong, Linwei, Qiu, Yi, Wang, Huawei, Gao, Fengqiang, Wen, Zongheng, Zhan, Choujun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270443/
https://www.ncbi.nlm.nih.gov/pubmed/35803972
http://dx.doi.org/10.1038/s41598-022-15855-7
_version_ 1784744470501130240
author Guo, Yijing
Zhong, Linwei
Qiu, Yi
Wang, Huawei
Gao, Fengqiang
Wen, Zongheng
Zhan, Choujun
author_facet Guo, Yijing
Zhong, Linwei
Qiu, Yi
Wang, Huawei
Gao, Fengqiang
Wen, Zongheng
Zhan, Choujun
author_sort Guo, Yijing
collection PubMed
description Surface defect detection is a vital process in industrial production and a significant research direction in computer vision. Although today’s deep learning defect detection methods based on computer vision can achieve high detection accuracy, they are mainly based on supervised learning. They require many defect samples to train the model, which is not compatible with the current situation that industrial defect sample is difficult to obtain and costly to label. So we propose a new unsupervised small sample defect detection model-ISU-GAN, which is based on the CycleGAN architecture. A skip connection, SE module, and Involution module are added to the Generator, enabling the feature extraction capability of the model to be significantly improved. Moreover, we propose an SSIM-based defect segmentation method that applies to GAN-based defect detection and can accurately extract defect contours without the need for redundant noise reduction post-processing. Experiments on the DAGM2007 dataset show that the unsupervised ISU-GAN can achieve higher detection accuracy and finer defect profiles with less than 1/3 of the unlabelled training data than the supervised model with the full training set. Relative to the supervised segmentation models UNet and ResUNet++ with more training samples, our model improves the detection accuracy by 2.84% and 0.41% respectively and the F1 score by 0.025 and 0.0012 respectively. In addition, the predicted profile obtained using our method is closer to the real profile than other models used for comparison.
format Online
Article
Text
id pubmed-9270443
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-92704432022-07-10 Using ISU-GAN for unsupervised small sample defect detection Guo, Yijing Zhong, Linwei Qiu, Yi Wang, Huawei Gao, Fengqiang Wen, Zongheng Zhan, Choujun Sci Rep Article Surface defect detection is a vital process in industrial production and a significant research direction in computer vision. Although today’s deep learning defect detection methods based on computer vision can achieve high detection accuracy, they are mainly based on supervised learning. They require many defect samples to train the model, which is not compatible with the current situation that industrial defect sample is difficult to obtain and costly to label. So we propose a new unsupervised small sample defect detection model-ISU-GAN, which is based on the CycleGAN architecture. A skip connection, SE module, and Involution module are added to the Generator, enabling the feature extraction capability of the model to be significantly improved. Moreover, we propose an SSIM-based defect segmentation method that applies to GAN-based defect detection and can accurately extract defect contours without the need for redundant noise reduction post-processing. Experiments on the DAGM2007 dataset show that the unsupervised ISU-GAN can achieve higher detection accuracy and finer defect profiles with less than 1/3 of the unlabelled training data than the supervised model with the full training set. Relative to the supervised segmentation models UNet and ResUNet++ with more training samples, our model improves the detection accuracy by 2.84% and 0.41% respectively and the F1 score by 0.025 and 0.0012 respectively. In addition, the predicted profile obtained using our method is closer to the real profile than other models used for comparison. Nature Publishing Group UK 2022-07-08 /pmc/articles/PMC9270443/ /pubmed/35803972 http://dx.doi.org/10.1038/s41598-022-15855-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Guo, Yijing
Zhong, Linwei
Qiu, Yi
Wang, Huawei
Gao, Fengqiang
Wen, Zongheng
Zhan, Choujun
Using ISU-GAN for unsupervised small sample defect detection
title Using ISU-GAN for unsupervised small sample defect detection
title_full Using ISU-GAN for unsupervised small sample defect detection
title_fullStr Using ISU-GAN for unsupervised small sample defect detection
title_full_unstemmed Using ISU-GAN for unsupervised small sample defect detection
title_short Using ISU-GAN for unsupervised small sample defect detection
title_sort using isu-gan for unsupervised small sample defect detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270443/
https://www.ncbi.nlm.nih.gov/pubmed/35803972
http://dx.doi.org/10.1038/s41598-022-15855-7
work_keys_str_mv AT guoyijing usingisuganforunsupervisedsmallsampledefectdetection
AT zhonglinwei usingisuganforunsupervisedsmallsampledefectdetection
AT qiuyi usingisuganforunsupervisedsmallsampledefectdetection
AT wanghuawei usingisuganforunsupervisedsmallsampledefectdetection
AT gaofengqiang usingisuganforunsupervisedsmallsampledefectdetection
AT wenzongheng usingisuganforunsupervisedsmallsampledefectdetection
AT zhanchoujun usingisuganforunsupervisedsmallsampledefectdetection