Machine Learning System for Fabric Defect Detection and Classification - Pooja, Hattarki; soma, Shridevi (Journal of Neonatal Surgery, 2025)
The textile sector is crucial to India's economy, and one of its most significant facets is the management of fabric quality. In computer vision, texture analysis is used for the purposes of defect detection, classification, and segmentation. In order to save manufacturing time and costs, this paper explains a fundamental method for identifying various fabric flaws in the textile industry. An essential part of quality control, automated fabric inspection systems help find textile flaws quickly and accurately while also cutting down on human labor. In this work, we assess two classifiers—the NN classifier and the SVM classifier—on a 5000 fabric image samples, TILDA dataset for the purpose of recognizing six distinct defects: holes, horizontal defects, reed markings, burls, slubs, stains, and double end marks. One of the most fundamental and significant components of modern fashion is textile. We can't fathom a world devoid of textiles. Another essential problem in the textile production sector is fabric quality monitoring. When it comes to finding various types of fabric defects, such as holes, slubs, oil stains, etc., automatic defect detection is seen to be quite interesting. Using the provided fabric samples, this study introduces a novel method for fault and defect identification. The five-step process for textile defect detection begins with collecting picture samples from the industry-standard TILDA dataset. Grayscale transformation is a preprocessing technique that is used to enhance the picture quality and eliminate undesired noise. As a last step in feature extraction, SVM takes the gray-level co-occurrence matrix (GLCM) into account. The testing phase, however, involves validating these two classifiers using the test data and calculating their sensitivity, specificity, and accuracy.