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.

翻译：本文介绍了Kolmogorov-Arnold网络（KAN）在金属表面缺陷分类中的应用。具体而言，我们分析了钢材表面以检测裂纹、夹杂、斑块、点蚀和划痕等缺陷。基于Kolmogorov-Arnold定理，KAN提供了一种相较于传统多层感知机（MLP）的新方法，通过利用样条函数实现了更高效的函数逼近。结果表明，KAN网络能够以更少的参数获得比卷积神经网络（CNN）更高的准确率，从而在图像分类任务中实现更快的收敛速度和更好的性能。