Steel Surface Defect Segmentation Using U-Net with SLMA-Gray Attention and AdamW Optimization
Abstract
Rust detection on metal surfaces is a challenging task in industrial maintenance and quality control. This paper proposes an improved U-Net segmentation method that enhances the accuracy of surface defect detection in steel materials. This paper proposes an improved U-Net model for steel surface defect segmentation. We introduce a novel Spatial-Channel Gray-Level Mixed Attention mechanism (SLMA-Gray) to enhance defect saliency in grayscale images and employ the AdamW optimizer to improve generalization. Experiments on the NEU-DET dataset show our method significantly outperforms the original U-Net, with increases of 5.29% in precision, 18.84% in recall, 3.34% in accuracy, 15.17% in Dice, and 18.18% in IoU.
DOI:
https://doi.org/10.31449/inf.v49i31.10673Downloads
Published
12/23/2025
How to Cite
Yang, R., Chen, Z., Qin, Z., Lu, Y., & Qi, L. (2025). Steel Surface Defect Segmentation Using U-Net with SLMA-Gray Attention and AdamW Optimization. Informatica, 49(31). https://doi.org/10.31449/inf.v49i31.10673
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Section
Online-only
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