This project presents an AI-based approach to detect and measure defects in multi-patterned fabrics using computer vision, deep learning, and Explainable AI (XAI). Traditional fabric inspection processes rely heavily on manual labour, which is often slow, inconsistent, and prone to error—especially when dealing with complex or coloured patterns. Furthermore, manual inspection typically lacks precise defect sizing, which is critical for fabric quality evaluation based on standards like the 4-point system. To address these limitations, a deep learning model based on the Xception architecture was trained on a publicly available patterned fabric dataset to classify fabric patterns and detect common defect types such as holes, stains, and multiple defects. Grad-CAM, a popular XAI method, was used to generate visual explanations of the model’s predictions, improving transparency and user trust. These heatmaps also enabled the localisation and measurement of defects, which were further converted from pixel dimensions into real-world units using camera parameters including optical working distance and focal length. The model achieved 0.91 training accuracy and 0.88 accuracy on both validation and test sets for defect detection, with precision, recall, and F1-score also at 0.88. Pattern classification reached 0.98 test accuracy. This integrated system not only automates defect detection and sizing but also improves interpretability, making it a reliable and scalable tool for quality control in modern textile manufacturing.