Unsupervised fabric defect detection algorithm based on vector quantization and feature distance.
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| Title: | Unsupervised fabric defect detection algorithm based on vector quantization and feature distance. |
|---|---|
| Authors: | Wei, Qiyu1 (AUTHOR), Zhang, Lei1 (AUTHOR), Zhan, Zhu2 (AUTHOR), Li, Liqing1 (AUTHOR), Wang, Jun1 (AUTHOR) junwang@dhu.edu.cn |
| Source: | Textile Research Journal. May2026, Vol. 96 Issue 9/10, p967-981. 15p. |
| Subjects: | Vector quantization, Outlier detection, Deep learning, Machine learning |
| Abstract: | Fabric defect detection is an indispensable step in textile fabric production, and many deep-learning-based methods have been proposed. However, existing supervised methods are limited by the lack of annotated datasets, and unsupervised anomaly detection methods still fail to meet the requirements of practical applications. To address these issues, a novel unsupervised dual-scale method based on feature distance, called DSFD, is proposed. First, leveraging the periodic characteristics of fabric textures, a method for obtaining feature templates of image patches and a method for calculating anomaly scores of image patches are proposed based on a vector quantized variational autoencoder (VQ-VAE). Second, to address the issue that codebook vectors cannot be effectively activated using Euclidean distance-based quantization mechanism during model training and testing, a cosine similarity-based quantization mechanism is proposed. Ablation experiments demonstrate its effectiveness in improving defect detection performance. Finally, to enhance the robustness of the model when applied to different types of fabric images, a dual-scale method is proposed. The proposed method was compared with five state-of-the-art anomaly detection methods on three open-source datasets, achieving superior defect detection performance. It demonstrated a performance improvement of 2.1% in image-level defect detection and 1.3% in pixel-level segmentation in terms of area under the curve scores. [ABSTRACT FROM AUTHOR] |
| Copyright of Textile Research Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193712119 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unsupervised fabric defect detection algorithm based on vector quantization and feature distance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Qiyu%22">Wei, Qiyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhan%2C+Zhu%22">Zhan, Zhu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Liqing%22">Li, Liqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jun%22">Wang, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> junwang@dhu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Textile+Research+Journal%22">Textile Research Journal</searchLink>. May2026, Vol. 96 Issue 9/10, p967-981. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Fabric defect detection is an indispensable step in textile fabric production, and many deep-learning-based methods have been proposed. However, existing supervised methods are limited by the lack of annotated datasets, and unsupervised anomaly detection methods still fail to meet the requirements of practical applications. To address these issues, a novel unsupervised dual-scale method based on feature distance, called DSFD, is proposed. First, leveraging the periodic characteristics of fabric textures, a method for obtaining feature templates of image patches and a method for calculating anomaly scores of image patches are proposed based on a vector quantized variational autoencoder (VQ-VAE). Second, to address the issue that codebook vectors cannot be effectively activated using Euclidean distance-based quantization mechanism during model training and testing, a cosine similarity-based quantization mechanism is proposed. Ablation experiments demonstrate its effectiveness in improving defect detection performance. Finally, to enhance the robustness of the model when applied to different types of fabric images, a dual-scale method is proposed. The proposed method was compared with five state-of-the-art anomaly detection methods on three open-source datasets, achieving superior defect detection performance. It demonstrated a performance improvement of 2.1% in image-level defect detection and 1.3% in pixel-level segmentation in terms of area under the curve scores. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Textile Research Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00405175251342617 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 967 Subjects: – SubjectFull: Vector quantization Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Unsupervised fabric defect detection algorithm based on vector quantization and feature distance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Qiyu – PersonEntity: Name: NameFull: Zhang, Lei – PersonEntity: Name: NameFull: Zhan, Zhu – PersonEntity: Name: NameFull: Li, Liqing – PersonEntity: Name: NameFull: Wang, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00405175 Numbering: – Type: volume Value: 96 – Type: issue Value: 9/10 Titles: – TitleFull: Textile Research Journal Type: main |
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