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FocusDet: towards high-quality digital printing fabric defect detection.

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Title: FocusDet: towards high-quality digital printing fabric defect detection.
Authors: Su, Zebin1,2,3 (AUTHOR), Lu, Yanjun1 (AUTHOR) yanjunlu@xaut.edu.cn, Wu, Jingwei2,3 (AUTHOR), Zhang, Huanhuan2,3 (AUTHOR), Li, Pengfei2,3 (AUTHOR)
Source: Textile Research Journal. Dec2023, Vol. 93 Issue 23/24, p5237-5248. 12p.
Subjects: Digital printing, Textiles, Problem solving, Product quality, Product improvement
Abstract: Deep-learning models have been effectively applied to the fabric defect detection field, in which dilemmas still exist for further improving product quality. For the self-built digital printing fabric defect detection dataset, the dilemmas can be expressed in aspects. First, the existing detection models are more inclined to learn many shot categories (head classes) and directly ignore low shot categories (tail classes); Second, the sampled positive and negative anchors in each mini-batch are not equally important, therefore they should be unequally attended to according to their importance. To solve these problems, in this article, a high-quality model for digital printing fabric defect detection was proposed, termed FocusDet. Specially, we construct the model based on the Faster-RCNN framework with two well-designed modules: the balanced group softmax module and the importance-based sample reweighting module, which improve the detection accuracy. Experimental results demonstrate that our proposed model reaches state-of-the-art accuracy on COCO metrics compared with other advanced detection models in the digital printing fabric defect detection dataset. [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
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: FocusDet: towards high-quality digital printing fabric defect detection.
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  Data: <searchLink fieldCode="JN" term="%22Textile+Research+Journal%22">Textile Research Journal</searchLink>. Dec2023, Vol. 93 Issue 23/24, p5237-5248. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+printing%22">Digital printing</searchLink><br /><searchLink fieldCode="DE" term="%22Textiles%22">Textiles</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Product+quality%22">Product quality</searchLink><br /><searchLink fieldCode="DE" term="%22Product+improvement%22">Product improvement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Deep-learning models have been effectively applied to the fabric defect detection field, in which dilemmas still exist for further improving product quality. For the self-built digital printing fabric defect detection dataset, the dilemmas can be expressed in aspects. First, the existing detection models are more inclined to learn many shot categories (head classes) and directly ignore low shot categories (tail classes); Second, the sampled positive and negative anchors in each mini-batch are not equally important, therefore they should be unequally attended to according to their importance. To solve these problems, in this article, a high-quality model for digital printing fabric defect detection was proposed, termed FocusDet. Specially, we construct the model based on the Faster-RCNN framework with two well-designed modules: the balanced group softmax module and the importance-based sample reweighting module, which improve the detection accuracy. Experimental results demonstrate that our proposed model reaches state-of-the-art accuracy on COCO metrics compared with other advanced detection models in the digital printing fabric defect detection dataset. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  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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      – Type: doi
        Value: 10.1177/00405175231196324
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 5237
    Subjects:
      – SubjectFull: Digital printing
        Type: general
      – SubjectFull: Textiles
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: Product quality
        Type: general
      – SubjectFull: Product improvement
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      – TitleFull: FocusDet: towards high-quality digital printing fabric defect detection.
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            NameFull: Su, Zebin
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            NameFull: Lu, Yanjun
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            NameFull: Wu, Jingwei
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            NameFull: Zhang, Huanhuan
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            NameFull: Li, Pengfei
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            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
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              Value: 93
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              Value: 23/24
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