Enhancing the Resilience of CNN-KAN Hybrid Architectures to Environmental Distortions in Traffic Sign Classification via Focused Data Augmentation
Abstract
This paper investigates the efficacy of integrating Kolmogorov-Arnold Networks (KANs) into deep learning architectures to enhance traffic sign recognition robustness against environmental distortions. We evaluate three architectures---a baseline CNN, a hybrid CNN-KAN, and a fully integrated CKAN---using a stochastic augmentation pipeline comprising rotation, simulated rain, blur, and brightness shifts. Experimental results on the GTSRB dataset demonstrate that the hybrid CNN-KAN achieves superior accuracy on clean dataset. KAN-based models excel at handling geometric transformations when trained with augmentation but exhibit higher sensitivity to signal degradation (heavy blur, low light) compared to standard convolutions. We conclude that while KANs offer sharper decision boundaries, their learnable activation functions require extensive augmentation to match the inherent stability of traditional CNNs in low-signal-to-noise environments.
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