Fabric defect detection based on information entropy and frequency domain saliency

Abstract

The automatic detection of defects is an important part of the fabric production process. However, existing methods of detecting defects in fabrics with periodic patterns lack adaptability and perform poorly in detection. In this paper, we propose an unsupervised fabric defect detection method based on the human visual attention mechanism. The method introduces two-dimensional entropy which can reflect the spatial distribution characteristics of images based on one-dimensional entropy, according to the relationship between information entropy and image texture. The image is reconstructed into a quaternion matrix by combining two-dimensional entropy and three feature maps that characterize the opponent color space representation of the input image. The hypercomplex Fourier transform is then used to transform the quaternion image matrix into the frequency domain. We propose a new method for local tuning of amplitude spectrum, thereby suppressing the background pattern while retaining the defect region. Finally, the inverse transform is performed to obtain a saliency map. Through experimental comparisons and a series of numerical evaluations, we demonstrate that the proposed method has a better detection effect compared to state-of-the-art methods in fabric defect detection.