Automated fabric defect inspection with fuzzy c-means, conditional random fields and refined U-Net

In this study, multiple segmentation methods were applied sequentially to enhance the classification for fabric data. First, fuzzy c-means (FCM) segmentation was employed on fabric images, effectively separating in homogeneous regions. Subsequently, these results were refined using conditional random fields (CRF), which provided sharper and more accurate delineation of pixel boundaries. In the final stage, I applied the refined U-Net model to the segmentation results enhanced by CRF, ensuring the preservation of small details in complex and irregularly structured fabric images. In industrial applications such as fabric flaw identification, the distinct contribution of categorizing based on the segmentation results significantly enhanced classification capabilities, particularly in low-quality images. This innovative hybrid strategy serves as an effective technique for improving outcomes, especially in images with uneven textures. Compared to the original dataset, the results are markedly better and pave the way for future advancements in industrial fabric analysis procedures. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2026.