Theoretical Study of Structural Defects in Textile Fabrics

This article broadly covers modern research directions for identifying and evaluating structural defects in textile fabrics. These defects significantly affect the quality, aesthetic appearance, and functional properties of fabrics. Therefore, detecting, diagnosing, and effectively eliminating them is one of the most pressing issues in the textile industry. The study explores advanced techniques for identifying structural defects using image processing algorithms, deep learning technologies, and neural networks. It examines the potential of a deep learning approach based on the Fisher criterion to ensure high accuracy in fabric quality diagnostics. New approaches using Gabor filters to analyze the spectral and geometric parameters of fabrics are also discussed. Methods aimed at improving the automated detection of defect shape, location, and density on fabric surfaces are presented. The research results include practical recommendations to optimize production processes and enhance quality control. In particular, it outlines opportunities to take significant steps in defect detection and prevention through the implementation of modern technologies. This study not only contributes to improving fabric quality but also lays a vital scientific foundation for the sustainable development of the textile industry.

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