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Appl. Sci. 2018, 8(9), 1628;

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

School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Author to whom correspondence should be addressed.
Received: 2 August 2018 / Revised: 6 September 2018 / Accepted: 9 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Intelligent Imaging and Analysis)
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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. View Full-Text
Keywords: surface defect of steel sheet; image segmentation; saliency detection; low-rank and sparse decomposition surface defect of steel sheet; image segmentation; saliency detection; low-rank and sparse decomposition

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Zhou, S.; Wu, S.; Liu, H.; Lu, Y.; Hu, N. Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet. Appl. Sci. 2018, 8, 1628.

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