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Masked contrastive generative adversarial network for defect detection of yarn-dyed fabric

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Abstract

Yarn-dyed Fabric have a wide variety of patterns. But in the production process, the defective yarn-dyed fabric is often a small amount, so the unsupervised defect detection method of yarn-dyed fabric is increasingly used. In this paper, we proposed an unsupervised defect detection network for yarn-dyed fabrics, which is called Masked Contrastive Generative Adversarial Network (MCGAN). MCGAN has two important parts: contrastive learning and NAM Mask Module. Contrastive learning can maximize the mutual information of features in the same position of input and output images and improve the training efficiency of the Convolutional Neural Networks (CNNs). However, both CNNs and contrastive learning lack the ability to model images globally and only focus on capturing local features. Therefore, Nam Mask Module is proposed. It combines masked convolution with Normalization-based Attention Module (NAM), which is a channel attention. Nam Mask Module reconstructs masked features through local features to allow the model to improve the ability to extract global features. Our proposed MCGAN model achieves excellent defect detection effect of yarn-dyed Fabrics on the public datasets YDFID and ZJU-Leaper.

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Authors and Affiliations

  1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, Shaanxi Province, China

    Hongwei Zhang, Zhidong Lu & Xiwei Chen

  2. Institute of Engineering Medicine, Beijing Institude of Technology, Beijing, 100081, China

    Shuai Lu

  3. School of Mathematics, Hangzhou Normal University, Hangzhou, 310058, Zhejiang Province, China

    Le Yao

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  1. Hongwei Zhang
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Correspondence to Hongwei Zhang.

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Zhang, H., Lu, Z., Chen, X. et al. Masked contrastive generative adversarial network for defect detection of yarn-dyed fabric. J Supercomput 81, 239 (2025). https://doi.org/10.1007/s11227-024-06711-8

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