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<p>One of the problematic issues in textile production is quality control in fancy yarn because of the complicated structure and aesthetic needs of these goods. This paper presents a novel integrated system that combines artificial intelligence and computer vision algorithms for quality assurance of fancy yarns. The proposed system has been designed, implemented as a laboratory prototype, and experimentally validated under controlled conditions. The implemented prototype demonstrates capabilities for defect detection, thickness measurement, pattern analysis, and 3D visualization using a combination of 2D image processing and laser profilometry data. Under controlled laboratory conditions (22 ± 2&#xa0;°C, 65 ± 5% RH), the suggested system demonstrates a defect detection accuracy of 94.7% (95%, Confidence Interval (CI) [94.1%, 95.3%]), thickness uniformity precision of 96.2%, and pattern regularity reliability of 92.5% (95% CI [91.2%, 93.8%]). The system operates on the plane yarn images in 8 states that comprise image acquisition, pre-processing, feature extraction, thickness checking, defect detection, pattern checking, quality checking and reporting. In accordance with the outcomes of the experiment, the suggested system demonstrates the accuracy of 94.7% in the defect detection, the precision of 96.2% in the thickness uniformity and the reliability of 92.5% in the pattern regularity. The integration of 3D visualization provides manufacturers with enhanced spatial understanding of yarn properties, enabling proactive quality management and reducing non-conformance to quality standards. The complete MATLAB R2024b implementation, including network architectures, training protocols, and evaluation routines, is provided in Appendix A to ensure reproducibility.</p>

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AI-powered industrial quality assurance system for fancy yarn using computer vision and 3D visualization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Shaymaa E. Sorour,
  • A. E. Amin

摘要

One of the problematic issues in textile production is quality control in fancy yarn because of the complicated structure and aesthetic needs of these goods. This paper presents a novel integrated system that combines artificial intelligence and computer vision algorithms for quality assurance of fancy yarns. The proposed system has been designed, implemented as a laboratory prototype, and experimentally validated under controlled conditions. The implemented prototype demonstrates capabilities for defect detection, thickness measurement, pattern analysis, and 3D visualization using a combination of 2D image processing and laser profilometry data. Under controlled laboratory conditions (22 ± 2 °C, 65 ± 5% RH), the suggested system demonstrates a defect detection accuracy of 94.7% (95%, Confidence Interval (CI) [94.1%, 95.3%]), thickness uniformity precision of 96.2%, and pattern regularity reliability of 92.5% (95% CI [91.2%, 93.8%]). The system operates on the plane yarn images in 8 states that comprise image acquisition, pre-processing, feature extraction, thickness checking, defect detection, pattern checking, quality checking and reporting. In accordance with the outcomes of the experiment, the suggested system demonstrates the accuracy of 94.7% in the defect detection, the precision of 96.2% in the thickness uniformity and the reliability of 92.5% in the pattern regularity. The integration of 3D visualization provides manufacturers with enhanced spatial understanding of yarn properties, enabling proactive quality management and reducing non-conformance to quality standards. The complete MATLAB R2024b implementation, including network architectures, training protocols, and evaluation routines, is provided in Appendix A to ensure reproducibility.