Methods of fabric defect detection using expert systems - a systematic literature review

Abstract

This study offers an extensive literature review on fabric defect detection techniques, commencing with a concise elucidation of fundamental components within the image acquisition system, including cameras and lenses. The defect detection methods are systematically classified into seven categories: structural, statistical, spectral, model-based, learning, hybrid, and comparative studies. Evaluation of these methods is conducted based on criteria encompassing accuracy, computational cost, reliability, rotational/scaling invariance, online/offline operational capabilities, and sensitivity to noise. The paper aims to provide a nuanced understanding of the efficacy of various fabric defect detection methodologies, offering insights into their strengths and limitations across diverse criteria.Fabric defect detection is a critical aspect of quality control in textile manufacturing, as it directly impacts the final product’s quality. Expert systems, leveraging advanced computational techniques and domainspecific knowledge, have emerged as promising tools for automating the detection of fabric defects. This systematic literature review aims to provide a comprehensive overview of the various methods employed in fabric defect detection using expert systems.