Abstract

Fabric defect detection plays a vital role in ensuring product quality and reducing production costs in the textile industry. With the advent of computer vision techniques, fabric defect detection has witnessed significant advancements, providing automated and accurate inspection capabilities. This research article presents a comprehensive review of the state-of-the-art computer vision techniques employed for fabric defect detection. We discuss various approaches, including image processing, machine learning, and deep learning, highlighting their strengths, limitations, and future directions. The aim of this article is to provide researchers and industry professionals with a comprehensive understanding of the current landscape and inspire further innovation in this field. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentationbased approaches, frequency domain operations, texture-based defect detection, sparse feature based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.