A Fuzzy U-Network-based Clustering Approach for Defect Detection in Fabrics

The Fabric Defect (FD) classification system lowers the expenses related to low-quality products by improving productivity and product quality. There are quite a number of existing fabric defect classification techniques, but most of them are not suitable for classification in a complex environment due to insufficient deep feature extraction. Fabric defect detection is difficult in the fabric industry due to complex shapes and a variety of defects. In this research, a novel clustering-based segmentation technique is proposed to segment fabric defects. Initially, the input fabric images are denoised utilizing the Wiener filter to reduce noise artifacts. Using the pre-processed images, the defective and normal regions are segmented using Fuzzy k-means clustering. The defective regions are identified and analyzed, while the normal regions are further classified using a Random Forest classifier to detect minute defects more accurately. Finally, both defective and normal fabric images are concatenated into the Fuzzy U-Net for fabric region segmentation. The proposed Fuzzy U-Net model attains a total accuracy (ACY) of 8.87%, 8.34%, 2.85%, and 4.51% better than DeepLabV3, seg net, Mask RCNN, and U-net, respectively. The proposed Clustering-based segmentation model enhances the total ACY by 30.14%, 14.65%, 55.16%, and 11.98% better than CNN, MSCDAE, FabricNet, and FN-Net, respectively. The proposed clustering-based segmentation technique yields higher accuracy than the existing models. Compared to CNNs and other deep learning models, the clustering-based segmentation significantly improves accuracy. Real-time fabric inspection is possible with it since it allows accurate defect detection in complex environments.