A Novel Patterned Fabric Defect Detection Algorithm based on GHOG and Low-rank Recovery

In order to accurately detect the patterned fabric defects, a novel patterned fabric detection algorithm based on Gabor-HOG (GHOG) and low-rank recovery is proposed. Firstly, Gabor filter preprocess the pattern fabric image to generate the Gabor maps, and then HOG feature is extracted from the blocks of Gabor maps with size of 16x16. Secondly, the feature vectors GHOG of all blocks is stacked into a feature matrix, each column represents an image block. Thirdly, an efficient low rank recovery model is built for the feature matrix, and is decomposed into a low-rank matrix (background information) and a sparse matrix (defect information) by the alternative direction multiplier method (ADMM). Finally, the saliency map generated by sparse matrix is segmented by the improved optimal threshold algorithm, to locate the defect regions. Experiment results show that recovery the proposed method can effectively detect patterned fabric defect, and outperforms the state-of-the-art methods.