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Unsupervised fabric defect detection with local spectra refinement (LSR)

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Abstract

The inspection of fabric defects is of great importance, as undetected and uncorrected defects entail poor production quality and expensive compensation. Due to the variety of defect types and sizes, it is a very tedious task to perform inspection manually. There are numerous automated systems in the literature; however, most of them require a training scheme where clean and defective fabric samples are manually fed to the system. Because of the diversity of fabric patterns and defect classes, supervised systems reduce convenience and ease of use in real practice. In this study, we propose an unsupervised, robust fabric defect detection method using spectral domain analysis. The proposed algorithm has a very simple flow and can run without any prior training scheme. First, the algorithm splits the input textile image into smaller patches and computes a generic spectral representation of the fabric pattern. Then, the method detects defective regions by measuring dissimilarities between the spectral representation and all local patches of the input fabric. We also introduce a textile fabric dataset, i.e., Ten Fabrics Dataset, which consists of ten different types of fabrics with 27 of the most common textile defects. According to the extensive set of experiments on two different datasets, the proposed method outperforms the state-of-the-art by achieving up to 94% accuracy.

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Data availability

The source code of the proposed method (LSC method) is available at: (https://github.com/sahar-shakir/Local-spectra-Clustering-LSC-method) Ten Fabric defect dataset used in this study can be found at: (https://www.kaggle.com/saharshakir/ten-fabrics-dataset-tfd) All other data are available from the authors upon request.

Notes

  1. https://www.kaggle.com/saharshakir/ten-fabrics-dataset-tfd.

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Authors and Affiliations

  1. Technical Engineering College - Kirkuk, Northern Technical University, Kirkuk, Iraq

    Sahar Shakir

  2. Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Türkiye

    Cihan Topal

Authors
  1. Sahar Shakir
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  2. Cihan Topal
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Correspondence to Sahar Shakir.

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Shakir, S., Topal, C. Unsupervised fabric defect detection with local spectra refinement (LSR). Neural Comput & Applic 36, 1091–1103 (2024). https://doi.org/10.1007/s00521-023-09080-0

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