A Masked Autoencoder Approach to Unsupervised Steel Surface Defect Recognition
A Transformer-based Masked Autoencoder learns representations for unsupervised steel surface defect recognition. Pretraining masks 75% of image patches, a lightweight decoder reconstructs them, and an auxiliary defect localization objective is jointly trained. Decoder achieves SSIM 0.92, MSE 0.47, and clustering yields 91.3% Hungarian matched accuracy on six defect categories.
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[Submitted on 14 Jul 2026]
Title:A Masked Autoencoder Approach to Unsupervised Steel Surface Defect Recognition
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Abstract:Automated visual inspection of steel surface defects is a recurring quality control task in which labeled defect data is scarce and costly to obtain, while unlabeled surface images are abundant, which motivates self supervised methods that learn useful representations without class labels. A Transformer based Masked Autoencoder is used here to learn representations of steel surface defects for unsupervised grouping. During pretraining, 75% of the input image patches are randomly masked, and a lightweight decoder reconstructs the masked regions from the visible 25%. The encoder is trained jointly with an auxiliary defect localization objective, used only as a training signal and not evaluated as a detector. The decoder reaches a structural similarity score of 0.92 and a mean squared error of 0.47. Features from the pretrained encoder are then clustered using UMAP for dimensionality reduction and Agglomerative clustering, reaching a Hungarian matched accuracy of 91.3% against the six known defect categories.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.13178 [cs.CV]
(or arXiv:2607.13178v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.13178
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shrey Patel Mr. [view email] [v1] Tue, 14 Jul 2026 18:26:36 UTC (5,057 KB)
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