Robustness Analysis of CNN and Convolutional KAN Architectures against Weather and Geometric Distortions in Traffic Sign Recognition
Abstract
This paper investigates the robustness of traffic sign classification models against real-world visual disturbances. We conduct a comparative evaluation of three distinct architectures: a standard CNN, a hybrid CNN enhanced with Kolmogorov-Arnold dense layers (CNN-KAN), and a fully convolutional Kolmogorov-Arnold Network (CKAN). Unlike traditional CNNs, the KA-based models utilize learnable activation functions, potentially offering improved resilience. The experiments were conducted using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, containing 43 classes of traffic signs. Models were trained and tested on both original images and versions degraded by controlled disturbances, including rotation, blur, brightness variation, and simulated rain. The results demonstrate that the proposed CNN-KAN model provides consistently superior performance under small-to-moderate rotations (up to 20 degrees) and moderate brightness increases, achieving the highest accuracy in all rain-mask scenarios. It remains competitive under blur, where it ranks second only to the standard CNN. Performance decreases were observed only at extreme brightness levels, where both the standard CNN and CKAN maintained higher stability. Overall, the findings highlight the potential of Kolmogorov-Arnold-based architectures for improving robustness in traffic sign recognition systems operating under realistic and dynamically changing environmental conditions.
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