Stiffness Analysis of Wearing Fabrics Based on Singular Value Decomposition Method

To address the issues of high cost and low accuracy in the manual detection method, an improved singular value decomposition (SVD)-based fabric defect detection method was proposed in this study. The method first performed noise reduction by wavelet transform; then the image was segmented. Finally, SVD was applied to remove background texture information and improve detection accuracy. The results for the detection of different types of fabric defects showed that the improved SVD method for stiffness detection of fabrics was highly efficient and accurate. The computational complexity, data redundancy and detection results of different sub-image sizes of pixels were all significant. The area under the curve (AUC) value of the star and check fabric was inferior to the defect fabric. The method is highly accurate for different fabric types and can be subsequently applied to the detection of stiffness in apparel fabrics, providing a reference for textile manufacturing production.