Neural Subspace Learning for Surface Defect Detection

Bin Liu, Weifeng Chen, Bo Li () and Xiuping Liu
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Bin Liu: School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China
Weifeng Chen: School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China
Bo Li: School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China
Xiuping Liu: School of Mathematical Science, Dalian University of Technology, Dalian 116024, China

Mathematics, 2022, vol. 10, issue 22, 1-16

Abstract: Surface defect inspection is a key technique in industrial product assessments. Compared with other visual applications, industrial defect inspection suffers from a small sample problem and a lack of labeled data. Therefore, conventional deep-learning methods depending on huge supervised samples cannot be directly generalized to this task. To deal with the lack of labeled data, unsupervised subspace learning provides more clues for the task of defect inspection. However, conventional subspace learning methods focus on studying the linear subspace structure. In order to explore the nonlinear manifold structure, a novel neural subspace learning algorithm is proposed by substituting linear operators with nonlinear neural networks. The low-rank property of the latent space is approximated by limiting the dimensions of the encoded feature, and the sparse coding property is simulated by quantized autoencoding. To overcome the small sample problem, a novel data augmentation strategy called thin-plate-spline deformation is proposed. Compared with the rigid transformation methods used in previous literature, our strategy could generate more reliable training samples. Experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance compared with unsupervised methods. More importantly, the proposed method is competitive and has a better generalization capability compared with supervised methods based on deep learning techniques.

Keywords: deep learning; thin-plate-spline; auto-encoder; low-rank; defect detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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