Fabric defect detection based on low-rank decomposition with factor group-sparse regularizer

Recently, many low-rank-based methods in terms of detecting defects in fabric images have been proposed. However, there are two disadvantages of these methods. First, current low-rank based methods use the nuclear norm as the surrogate of rank, which causes inefficient optimization process and sub-optimal performance. Second, low-rank defective regions cannot be detected by low-rank based models. Thus, we propose a factor group-sparse regularized low-rank decomposition model (FGSRLRD) to solve these problems. This method takes the factor group-sparse regularizer as the surrogate of rank, which is more efficient as singular value decomposition (SVD) is not applied in the optimization process. Better performance is achieved as the factor group-sparse regularizer is a more accurate approximation of the rank. In addition, the weight matrix generated by the lightweight autoencoder is incorporated into the object function of FGSRLRD to guide locating defective regions. Besides, as low-rank defective regions cannot be segmented by low-rank models, this method constructs a fusion image of the prior image and the sparse image to highlight the defective regions. The performance of the proposed method is evaluated on two standard datasets, and the results indicate that the suggested method outperforms the existing state-of-the-art methods in locating the defective regions on fabric images.