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Inspection of textile fabrics based on the optimal Gabor filter

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Abstract

Defect detection and quality control play an important role in the textile industry. In this paper, an automatic algorithm based on the optimal Gabor filter is proposed for real-time inspection of textile fabrics. The Cuckoo optimization algorithm is adopted to optimize the parameters of the Gabor filter. Also, an adaptive local binarization method is proposed, which enhances the performance of our algorithm. In order to locate the defects, the filtered image is divided into non-overlapping blocks. Then, the candidate defective blocks are binarized using adaptive thresholds, which are determined by blocks statistics. The performance of the proposed algorithm is evaluated through different types of fabrics in the TILDA database and an online Fabric Stain Dataset. The experimental results demonstrate the efficiency of the proposed method in detecting defects on the plain, regular and irregular patterned fabrics. Furthermore, the comparative results are provided to show the robustness of the proposed method.

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Acknowledgement

The authors would like to thank the Workgroup on Texture Analysis of the DFG (Deutsche Forschungsgemeinschaft Germany) for providing the TILDA Textile Texture Database.

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Authors and Affiliations

  1. Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Mina Boluki & Farahnaz Mohanna

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  1. Mina Boluki
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  2. Farahnaz Mohanna
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Correspondence to Farahnaz Mohanna.

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Boluki, M., Mohanna, F. Inspection of textile fabrics based on the optimal Gabor filter. SIViP 15, 1617–1625 (2021). https://doi.org/10.1007/s11760-021-01897-3

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