Kolmogorovarnold Networks For Metal Surface Defect Classification
ASecurity
**arXiv ID:** 2501.06389 **Authors:** Maciej Krzywda, Mariusz Wermiński, Szymon Łukasik, Amir H. Gandomi **Published:** 2025-01-10T23:58:30Z **Abstract:** This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multil...
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# Kolmogorov-Arnold networks for metal surface defect classification
**arXiv ID:** 2501.06389
**Authors:** Maciej Krzywda, Mariusz Wermiński, Szymon Łukasik, Amir H. Gandomi
**Published:** 2025-01-10T23:58:30Z
**Abstract:**
This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multilayer perceptrons (MLPs), facilitating more efficient function approximation by utilizing spline functions. The results show that KAN networks can achieve better accuracy than convolutional neural networks (CNNs) with fewer parameters, resulting in faster convergence and improved performance in image classification.
## Skill Description
This skill is generated from the arXiv paper: Kolmogorov-Arnold networks for metal surface defect classification (2501.06389).
## How to Use
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## References
- [arXiv:2501.06389](http://arxiv.org/abs/2501.06389v1)
