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A new method for detecting texture defects based on modified local binary pattern

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Abstract

The modified local binary pattern is a method that can produce high-precision features for detection and diagnosis of texture images; in this paper, a method is proposed to detect the texture defects based on this algorithm. The proposed method includes two main phases. The first phase is based on clustering technique to fabric normal texture modeling, and the second phase is a threshold to decide about the fabric defects selection. The total dataset in this research contains 596 texture images from different databases including Isfahan textile dataset, UHK dataset, products and TILDA dataset. The fabric defects are generated because of pressure cracks and has effects, woof defects, warp defects and spool slacking. Finally, a noticeable detection rate about 91.86% with a higher rate of 92.02% sensitivity is achieved for the total given dataset. All of the reported results from tests are achieved by applying the proposed method on the explained dataset.

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Authors and Affiliations

  1. Young Researchers and Elite Club, Islamic Azad University, Majlesi Branch, Isfahan, Iran

    Mohammad Makaremi

  2. Department of Electrical Engineering, University of Tafresh, Tafresh, Iran

    Navid Razmjooy & Mehdi Ramezani

  3. Department of Mathematics, University of Tafresh, Tafresh, Iran

    Mehdi Ramezani

Authors
  1. Mohammad Makaremi
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  2. Navid Razmjooy
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  3. Mehdi Ramezani
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Correspondence to Navid Razmjooy.

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Makaremi, M., Razmjooy, N. & Ramezani, M. A new method for detecting texture defects based on modified local binary pattern. SIViP 12, 1395–1401 (2018). https://doi.org/10.1007/s11760-018-1294-9

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  • DOI: https://doi.org/10.1007/s11760-018-1294-9

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