Kolmogorov-Arnold networks for metal surface defect classification

Kolska 12, Warsaw 01-045, Poland Abstarct: 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. In recent years, there has been a growing 1. Introduction Among the promising continuous advancements in neural network architectures alternatives to traditional Multilayer Perceptron (MLPs), significantly contributing to progress in the image Kolmogorov-Arnold Networks (KANs) leverage the classification field [1,2,3].