Work

Small Defects Detection of Galvanized Strip Steel via Schatten-p Norm-Based Low-Rank Tensor Decomposition

Year: 2025

Type: article

Abstract: 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’s 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 surface defect image, a Schatten-p norm-based low-rank tensor decomposition (SLRTD) method is proposed to decomposes 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 defect image. Then, considering the over-shrinkage problem in low-rank component estimation caused by vanilla nuclear norm and weighted nuclear norm, a nonlinear reweighting strategy based on Schatten p-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 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.

Source: Sensors

DOI: 10.3390/s25082606

Authors: Shiyang Zhou, Xuguo Yan, Huaiguang Liu, Caiyun Gong

Institutions: Wuhan University of Science and Technology, Huazhong University of Science and Technology

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Innovative Research Group Project of the National Natural Science Foundation of China, National Natural Science Foundation of China, Huazhong University of Science and Technology, Natural Science Foundation of Hubei Province