Defect detection for patterned fabric images based on GHOG and low-rank decomposition

TL;DR

This paper introduces a novel defect detection method for patterned fabric images using a combined Gabor-HOG descriptor and low-rank decomposition, improving accuracy and efficiency over existing techniques.

Contribution

The paper proposes a new directional descriptor GHOG and an efficient low-rank decomposition model with nonconvex surrogate for defect detection in patterned fabrics.

Findings

Effective detection of fabric defects demonstrated

Outperforms state-of-the-art methods

High computational efficiency achieved

Abstract

In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper. Defect-free pattern fabric images have the specified direction, while defects damage their regularity of direction. Therefore, a direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor and HOG, which is extremely valuable for localizing the defect region. Upon devising a powerful directional descriptor, an efficient low-rank decomposition model is constructed to divide the matrix generated by the directional feature extracted from image blocks into a low-rank matrix (background information) and a sparse matrix (defect information). A nonconvex log det(.) as a smooth surrogate function for the rank instead of the nuclear norm is also exploited to improve the efficiency of the low-rank model. Moreover, the computational efficiency is further improved by utilizing the alternative direction method of multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix is segmented via the optimal threshold algorithm to locate the defect regions. Experimental results show that the proposed method can effectively detect patterned fabric defects and outperform the state-of-the-art methods.