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An edge‐aware‐based feature refinement algorithm for silk surface defect detection.

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Title: An edge‐aware‐based feature refinement algorithm for silk surface defect detection.
Authors: Zhang, Mingzhi1,2,3 (AUTHOR), Zhang, Jie1,2 (AUTHOR) mezhangjie@dhu.edu.cn, Zhu, Zixun1,2,4 (AUTHOR), Wang, Junliang1,2 (AUTHOR)
Source: Coloration Technology. Apr2026, Vol. 142 Issue 2, p245-258. 14p.
Subjects: Edge detection (Image processing)
Abstract: Fabric defect detection is essential for ensuring the quality of textiles, particularly when addressing lustre issues in silk materials and tiny, inconspicuous flaws. This paper introduces a saliency detection framework for silk surface defects, named EFRNet, which aims to overcome the limitations of existing technologies in handling such fabric imperfections. Firstly, a multilayered spatial domain edge perception method was designed. This method enhances the perception of defect edge details through edge‐aware units and integrates these detailed features with high‐level semantic information using cross‐layer connection strategies, thereby achieving precise detection of defect edges. Secondly, a context‐aware multilayer feature fusion technique is proposed, which includes attention‐induced feature aggregation units and global context differential units, aimed at optimising feature selection, reducing background interference and emphasising the salient features of fabric defects through differential analysis. Experiments have demonstrated that EFRNet excels in improving the accuracy of small defect detection and local detail recognition. Compared with existing methods, it significantly enhances the quality of saliency maps, proving its effectiveness in enhancing the adaptability and precision of fabric defect detection. [ABSTRACT FROM AUTHOR]
Copyright of Coloration Technology is the property of Wiley-Blackwell 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.)
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  Data: Fabric defect detection is essential for ensuring the quality of textiles, particularly when addressing lustre issues in silk materials and tiny, inconspicuous flaws. This paper introduces a saliency detection framework for silk surface defects, named EFRNet, which aims to overcome the limitations of existing technologies in handling such fabric imperfections. Firstly, a multilayered spatial domain edge perception method was designed. This method enhances the perception of defect edge details through edge‐aware units and integrates these detailed features with high‐level semantic information using cross‐layer connection strategies, thereby achieving precise detection of defect edges. Secondly, a context‐aware multilayer feature fusion technique is proposed, which includes attention‐induced feature aggregation units and global context differential units, aimed at optimising feature selection, reducing background interference and emphasising the salient features of fabric defects through differential analysis. Experiments have demonstrated that EFRNet excels in improving the accuracy of small defect detection and local detail recognition. Compared with existing methods, it significantly enhances the quality of saliency maps, proving its effectiveness in enhancing the adaptability and precision of fabric defect detection. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Coloration Technology is the property of Wiley-Blackwell 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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        Value: 10.1111/cote.12830
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        Text: English
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              Text: Apr2026
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              Y: 2026
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