A review of fabric defect detection in textile manufacturing

As the textile industry shifts towards smart manufacturing, the detection of fabric defects has emerged as a critical element in ensuring product quality, enhancing productivity, and reducing costs. Computer vision and machine learning techniques, particularly deep learning, have emerged as essential tools in modern textile defect detection. This article provides a review of previous research in this field, examining the technological development and future directions for fabric surface defect detection. The article begins by discussing the various stages of fabric defect detection, which are categorized into four phases: image acquisition, image preprocessing, feature extraction and selection, and classification and detection. It also explores the application of image processing technologies at each stage, offering a detailed analysis of the advantages and limitations of each method through a comprehensive comparison. In addition, this article organizes existing fabric defect datasets and discusses the importance and challenges of datasets in model training. Finally, the article discusses the key challenges in fabric defect detection and outlines potential research directions for the future. The goal of this article is to guide the textile industry in selecting and applying machine vision technology, and to support the development of intelligent fabric inspection systems.