A SYSTEMATIC REVIEW: FABRIC DEFECT DETECTION SYSTEM
Keywords:
fabric defect, manual inspection, quality control, defect detection, textile inspectionAbstract
This paper presents a comprehensive literature review of fabric defect detection methods. These defect detection methods are systematically classified into nine categories: Structural, Statistical, Spectral, Model-based, GLCM-Based, Learning, Sparse- Based Operation, Deep Learning Based and hybrid. 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. These Methods are robust and efficient fabric defect detection methods which are required to develop automated inspection techniques. 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 while eliminating Manual inspections process which is lacking accuracy and it is time consuming
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