Fabric defect image generation method based on the dual-stage W-net generative adversarial network
Xuejuan Hu,
Yifei Liang,
Hengliang Wang
et al.
Abstract:Due to the intricate and diverse nature of textile defects, detecting them poses an exceptionally challenging task. In comparison with conventional defect detection methods, deep learning-based defect detection methods generally exhibit superior precision. However, utilizing deep learning for defect detection requires a substantial volume of training data, which can be particularly challenging to accumulate for textile flaws. To augment the fabric defect dataset and enhance fabric defect detection accuracy, we… Show more
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