Double sparse low rank decomposition for irregular printed fabric defect detection

Abstract

In this paper, a double sparse low-rank decomposition method is proposed to defect detection for complex irregular printed fabrics. Firstly, a low rank decomposition model with double sparsity is established by taking the sparse components as the printing template prior and the difference between the defective printed fabric graph and the template fabric graph as the defect prior. Secondly, the decomposition is guided by printing prior and defect for the double sparse low rank decomposition model to obtain the saliency map of defects. Furthermore, the defect map is obtained by binarising the defect’s saliency map using the optimal threshold segmentation. Finally, the simulation results are compared with the four existing methods. The results show that the proposed algorithm can effectively detect defects in three types of irregular printed fabrics, such as small-size print, medium-size print, and complex-distribution print. The valid positive rate is 89.29%, the false positive rate is 0.85%, and the positive predictive value is 86.21%. Comparison results show that the proposed algorithm retains the shape details of the defect better than the other four algorithms, and the detection time is 12.49% less than the current optimal PN-RPCA algorithm.