Next Article in Journal
Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features
Next Article in Special Issue
Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net
Previous Article in Journal
Numerical Investigations on Melting Behavior of Phase Change Material in a Rectangular Cavity at Different Inclination Angles
Previous Article in Special Issue
Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1628; https://doi.org/10.3390/app8091628

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)
Full-Text   |   PDF [5451 KB, uploaded 12 September 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top