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AdaptiveDet: Defect Detection for Digital Printing Fabric with Complex Background.

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Title: AdaptiveDet: Defect Detection for Digital Printing Fabric with Complex Background.
Authors: Su, Zebin1,2 (AUTHOR), Zhang, Xingyi1,2 (AUTHOR), Li, Jiamin1,2 (AUTHOR), Shao, Yunlong1,2 (AUTHOR), Li, Pengfei1,2 (AUTHOR) li6208@163.com, Zhang, Huanhuan1,2 (AUTHOR)
Source: Journal of Natural Fibers. Dec2025, Vol. 22 Issue 1, p1-13. 13p.
Subjects: Digital printing, Detection algorithms, Manufacturing defects, Defect tracking (Computer software development), Object recognition (Computer vision), K-means clustering
Abstract (English): During the digital printing process, the fabric defects need to be accurately detected to ensure product quality. However, the defects are difficult to effectively distinguish from the background, which can cause degradation of detection model performance. To solve this problem, a defect detection model incorporating adaptive attention mechanisms, AdaptiveDet, was proposed for digital printing fabric. First, the initial anchor box was generated using the K-means++ algorithm to better adapt to the complex target shape. Second, the backbone network could be reconfigured using the adaptive CBS module, allowing higher-level features to be extracted and interference with non-critical features to be reduced. Then, the neck network was reconfigured using the ELAN-EVC module so that the model could learn both global and local feature representations to capture information more accurately about minor defects. Finally, the DyHead framework was adopted in the head of YOLOv7-Tiny to enhance the model's sensitivity to spatial information, which lead to excellent performance in the complex background defect detection task. The experimental results show that the proposed model performs well on the DPFD-DET dataset with mAP@.5 of 93%, which outperforms other detection models. This shows that it could meet the demand for high-precision defect detection for digital printing fabric. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 在数字印刷过程中，需要准确检测织物缺陷以确保产品质量. 然而，这些缺陷很难与背景有效区分，这可能会导致检测模型性能的下降. 为了解决这个问题，提出了一种结合自适应注意力机制的数字印花织物缺陷检测模型AdaptiveDet. 首先，使用K-means++算法生成初始锚箱，以更好地适应复杂的目标形状. 其次，可以使用自适应CBS模块重新配置骨干网，从而提取更高级的特征并减少对非关键特征的干扰. 然后，使用ELAN-EVC模块重新配置颈部网络，以便模型可以学习全局和局部特征表示，从而更准确地捕获有关轻微缺陷的信息. 最后，YOLOv7 Tiny的头部采用了DyHead框架，以提高模型对空间信息的敏感性，从而在复杂的背景缺陷检测任务中取得了优异的性能. 实验结果表明，所提出的模型在DPFD-DET数据集上表现良好mAP@.593%，优于其他检测模型. 这表明它可以满足数字印花织物高精度缺陷检测的需求. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Natural Fibers is the property of Taylor & Francis 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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PubType: Academic Journal
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  Data: AdaptiveDet: Defect Detection for Digital Printing Fabric with Complex Background.
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  Data: <searchLink fieldCode="AR" term="%22Su%2C+Zebin%22">Su, Zebin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xingyi%22">Zhang, Xingyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiamin%22">Li, Jiamin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shao%2C+Yunlong%22">Shao, Yunlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Pengfei%22">Li, Pengfei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> li6208@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huanhuan%22">Zhang, Huanhuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Natural+Fibers%22">Journal of Natural Fibers</searchLink>. Dec2025, Vol. 22 Issue 1, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+printing%22">Digital printing</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+defects%22">Manufacturing defects</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: During the digital printing process, the fabric defects need to be accurately detected to ensure product quality. However, the defects are difficult to effectively distinguish from the background, which can cause degradation of detection model performance. To solve this problem, a defect detection model incorporating adaptive attention mechanisms, AdaptiveDet, was proposed for digital printing fabric. First, the initial anchor box was generated using the K-means++ algorithm to better adapt to the complex target shape. Second, the backbone network could be reconfigured using the adaptive CBS module, allowing higher-level features to be extracted and interference with non-critical features to be reduced. Then, the neck network was reconfigured using the ELAN-EVC module so that the model could learn both global and local feature representations to capture information more accurately about minor defects. Finally, the DyHead framework was adopted in the head of YOLOv7-Tiny to enhance the model's sensitivity to spatial information, which lead to excellent performance in the complex background defect detection task. The experimental results show that the proposed model performs well on the DPFD-DET dataset with mAP@.5 of 93%, which outperforms other detection models. This shows that it could meet the demand for high-precision defect detection for digital printing fabric. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 在数字印刷过程中，需要准确检测织物缺陷以确保产品质量. 然而，这些缺陷很难与背景有效区分，这可能会导致检测模型性能的下降. 为了解决这个问题，提出了一种结合自适应注意力机制的数字印花织物缺陷检测模型AdaptiveDet. 首先，使用K-means++算法生成初始锚箱，以更好地适应复杂的目标形状. 其次，可以使用自适应CBS模块重新配置骨干网，从而提取更高级的特征并减少对非关键特征的干扰. 然后，使用ELAN-EVC模块重新配置颈部网络，以便模型可以学习全局和局部特征表示，从而更准确地捕获有关轻微缺陷的信息. 最后，YOLOv7 Tiny的头部采用了DyHead框架，以提高模型对空间信息的敏感性，从而在复杂的背景缺陷检测任务中取得了优异的性能. 实验结果表明，所提出的模型在DPFD-DET数据集上表现良好mAP@.593%，优于其他检测模型. 这表明它可以满足数字印花织物高精度缺陷检测的需求. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Natural Fibers is the property of Taylor & Francis 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:
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    Identifiers:
      – Type: doi
        Value: 10.1080/15440478.2025.2454268
    Languages:
      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: Digital printing
        Type: general
      – SubjectFull: Detection algorithms
        Type: general
      – SubjectFull: Manufacturing defects
        Type: general
      – SubjectFull: Defect tracking (Computer software development)
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: K-means clustering
        Type: general
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      – TitleFull: AdaptiveDet: Defect Detection for Digital Printing Fabric with Complex Background.
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            NameFull: Su, Zebin
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            NameFull: Zhang, Xingyi
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            NameFull: Shao, Yunlong
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            NameFull: Li, Pengfei
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              M: 12
              Text: Dec2025
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              Y: 2025
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