KAN模型用样条函数提升金属缺陷分类精度与效率
Kolmogorov-Arnold networks for metal surface defect classification
- 用样条函数替代传统神经元，基于柯尔莫戈罗夫定理优化函数逼近
- 在缺陷分类任务中准确率高于CNN，参数量减少30%以上
- 适合需要轻量化高精度的工业质检场景
本文将柯尔莫戈罗夫-阿诺德网络（Kolmogorov-Arnold Networks, KAN）应用于金属表面缺陷分类，针对钢板上的裂纹、夹杂物、斑点、麻点和划痕等缺陷进行分析。基于柯尔莫戈罗夫-阿诺德表示定理，KAN采用样条函数实现更高效的函数逼近，相比传统多层感知机（MLP）具有更强表达能力。实验表明，KAN在图像分类任务中可实现优于卷积神经网络（CNN）的准确率，且参数量显著减少，收敛速度更快，性能更优。
原文摘要 · Abstract (English)
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.
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