Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition
Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production. It is a fundamental but “hard” small target detection problem due to its small pixel occupation in low-contrast images. By fully...
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MDPI AG
2025-04-01
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| author | Shiyang Zhou Xuguo Yan Huaiguang Liu Caiyun Gong |
| author_facet | Shiyang Zhou Xuguo Yan Huaiguang Liu Caiyun Gong |
| author_sort | Shiyang Zhou |
| collection | DOAJ |
| description | Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production. It is a fundamental but “hard” small target detection problem due to its small pixel occupation in low-contrast images. By fully exploiting the low-rank and sparse prior information of a surface defect image, a Schatten-<i>p</i> norm-based low-rank tensor decomposition (SLRTD) method is proposed to decompose the defect image into low-rank background, sparse defect, and random noise. Firstly, the original defect images are transformed into a new patch-based tensor mode through data reconstruction for mining valuable information of the defect image. Then, considering the over-shrinkage problem in the low-rank component estimation caused by a vanilla nuclear norm and a weighted nuclear norm, a nonlinear reweighting strategy based on a Schatten <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>p</mi></semantics></math></inline-formula>-norm is incorporated to improve the decomposition performance. Finally, a solution framework is proposed via a well-designed alternating direction method of multipliers to obtain the white-spot defect target image by a simple segmenting algorithm. The white-spot defect dataset from a real-world galvanized strip steel production line is constructed, and the experimental results demonstrate that the proposed SLRTD method outperforms existing state-of-the-art methods qualitatively and quantitatively. |
| format | Article |
| id | doaj-art-c357a1da277245efb7a3615a39cc7d3b |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-c357a1da277245efb7a3615a39cc7d3b2025-08-20T03:13:51ZengMDPI AGSensors1424-82202025-04-01258260610.3390/s25082606Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor DecompositionShiyang Zhou0Xuguo Yan1Huaiguang Liu2Caiyun Gong3Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaAccurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production. It is a fundamental but “hard” small target detection problem due to its small pixel occupation in low-contrast images. By fully exploiting the low-rank and sparse prior information of a surface defect image, a Schatten-<i>p</i> norm-based low-rank tensor decomposition (SLRTD) method is proposed to decompose the defect image into low-rank background, sparse defect, and random noise. Firstly, the original defect images are transformed into a new patch-based tensor mode through data reconstruction for mining valuable information of the defect image. Then, considering the over-shrinkage problem in the low-rank component estimation caused by a vanilla nuclear norm and a weighted nuclear norm, a nonlinear reweighting strategy based on a Schatten <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>p</mi></semantics></math></inline-formula>-norm is incorporated to improve the decomposition performance. Finally, a solution framework is proposed via a well-designed alternating direction method of multipliers to obtain the white-spot defect target image by a simple segmenting algorithm. The white-spot defect dataset from a real-world galvanized strip steel production line is constructed, and the experimental results demonstrate that the proposed SLRTD method outperforms existing state-of-the-art methods qualitatively and quantitatively.https://www.mdpi.com/1424-8220/25/8/2606galvanized strip steelsmall target detectionSchatten <i>p</i>-normtensor decomposition |
| spellingShingle | Shiyang Zhou Xuguo Yan Huaiguang Liu Caiyun Gong Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition Sensors galvanized strip steel small target detection Schatten <i>p</i>-norm tensor decomposition |
| title | Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition |
| title_full | Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition |
| title_fullStr | Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition |
| title_full_unstemmed | Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition |
| title_short | Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition |
| title_sort | small defects detection of galvanized strip steel via schatten i p i norm based low rank tensor decomposition |
| topic | galvanized strip steel small target detection Schatten <i>p</i>-norm tensor decomposition |
| url | https://www.mdpi.com/1424-8220/25/8/2606 |
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