A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection

Abstract

Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a wide array of colours and textile varieties, spanning a broad spectrum of fabrics. Due to the extensive diversity in colours, textures, and defect characteristics, fabric defect detection presents a complex and formidable challenge within the realm of patterned texture inspection. While recent trends have seen a rise in the utilization of deep learning methods for anomaly detection, there still exist notable gaps in this field. In this paper, we introduce a novel dataset comprising a diverse selection of fabrics and defects from a textile company based in Portugal. Our contributions encompass the provision of this unique dataset and the evaluation of state-of-the-art (SOTA) methods’ performance on our dataset.