Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

• Published in: Applied sciences Vol. 8; no. 9; p. 1628
• Main Authors: Zhou, Shiyang, Wu, Shiqian, Liu, Huaiguang, Lu, Yang, Hu, Nianzong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2018
• Subjects: Automation; Decomposition; Defects; Graphical representations; Image processing; image segmentation; low-rank and sparse decomposition; Metal sheets; Optimization; saliency detection; Scalars; Sparse matrices; Sparsity; Steel; surface defect of steel sheet; United States > US
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app8091628

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.