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<p>Color difference detection remains a critical challenge in textile manufacturing, where traditional visual inspection and offline measurement methods suffer from subjectivity, low efficiency, and delayed feedback. This study emphasizes engineering integration for online industrial fabric inspection rather than proposing a single new color-difference algorithm. The proposed system integrates a custom-designed optical acquisition platform with a lightweight color analysis pipeline, including bilateral filtering for noise suppression, K-means clustering for representative color extraction, RGB-to-CIELab color space conversion, and perceptually weighted <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\Delta E_{\textrm{CMC}(2:1)}\)</EquationSource></InlineEquation> computation. The system was deployed on an actual textile production line and evaluated using ten fabric rolls with different colors and materials. Experimental results show roll-level agreement with manual inspection in the tested samples and indicate the feasibility of continuous monitoring of chromatic variations along the fabric length. The proposed system provides a practical engineering solution for automated textile color quality control and may support production-line decision making while reducing dependence on subjective visual inspection in industrial environments.</p>

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Novel online fabric color difference detection system based on machine vision

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Ji-Huan Wang,
  • Yue Min,
  • Jin-Quan Xiong,
  • Ming Fang,
  • Mohd Shafry Mohd Rahim,
  • Dong-Lin Chen

摘要

Color difference detection remains a critical challenge in textile manufacturing, where traditional visual inspection and offline measurement methods suffer from subjectivity, low efficiency, and delayed feedback. This study emphasizes engineering integration for online industrial fabric inspection rather than proposing a single new color-difference algorithm. The proposed system integrates a custom-designed optical acquisition platform with a lightweight color analysis pipeline, including bilateral filtering for noise suppression, K-means clustering for representative color extraction, RGB-to-CIELab color space conversion, and perceptually weighted \(\Delta E_{\textrm{CMC}(2:1)}\) computation. The system was deployed on an actual textile production line and evaluated using ten fabric rolls with different colors and materials. Experimental results show roll-level agreement with manual inspection in the tested samples and indicate the feasibility of continuous monitoring of chromatic variations along the fabric length. The proposed system provides a practical engineering solution for automated textile color quality control and may support production-line decision making while reducing dependence on subjective visual inspection in industrial environments.