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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Multistage GAN for Fabric Defect Detection.

Juhua Liu, Chaoyue Wang, Hai Su

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 25, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a generative adversarial network (GAN) framework for advanced fabric defect detection. The system adapts to diverse textures and defects, improving automated quality control in textile manufacturing.

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    Area of Science:

    • Computer Vision
    • Materials Science
    • Artificial Intelligence

    Background:

    • Fabric defect detection is crucial for quality control but challenging due to complex textures and defect variations.
    • Existing methods often struggle with the diversity of fabrics and defect types, leading to suboptimal performance.

    Purpose of the Study:

    • To propose a robust fabric defect detection framework using a generative adversarial network (GAN).
    • To develop a system capable of adapting to various fabric textures and defect types in real-world applications.

    Main Methods:

    • A deep semantic segmentation network was customized for detecting different fabric defect types.
    • A multistage GAN was trained to synthesize realistic defects on defect-free fabric samples.
    • A texture-conditioned GAN explored defect distributions, and a GAN-based fusion network integrated generated defects.

    Main Results:

    • The proposed GAN-based framework demonstrated effective fabric defect detection capabilities.
    • The system showed adaptability to different fabric textures and defect characteristics.
    • Continuous updating of defect datasets and fine-tuning of the segmentation network improved detection under varied conditions.

    Conclusions:

    • The developed GAN-based framework offers a significant advancement in automated fabric defect detection.
    • The approach enhances adaptability and accuracy, addressing limitations of current methods.
    • This method contributes to improved quality control in the textile industry through intelligent defect analysis.