DK-Former: A Hybrid Structure of Deep Kernel Gaussian Process Transformer Network for Enhanced Traffic Sign Recognition

Traffic sign recognition is crucial for enhancing the safety and efficiency of intelligent transportation systems (ITS). This paper proposes a hybrid structure named DK-former, a lightweight and robust deep kernel Gaussian process transformer network, aiming to tackle non-linear small samples, high model parameters, and environmental variations, providing a distinctive solution that enhances robustness and adaptability in intelligent transportation systems. The whole structure is trained end-to-end based on the Bayesian framework. First, introducing convolutional random Fourier features facilitates effective non-linear sample feature mapping, capturing complex patterns inherent in traffic signs and enhancing computational efficiency. Second, the transformer model refines feature representations with its powerful self-attention mechanism to capture the global relationship between traffic sign pixels. Furthermore, the hybrid structure enhances the generalization capability of the model, allowing it to adapt to diverse scenarios, such as varying lighting conditions and weather fluctuations commonly encountered in real-world traffic environments. Extensive experiments have been conducted to validate the effectiveness of the proposed DK-former for traffic sign recognition. The model has achieved an outstanding recognition accuracy of 99.09% on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, superior to current state-of-the-art deep kernel learning traffic sign models with only 8.3 million parameters. Experiments on Indian and Chinese datasets comprehensively demonstrate the generalization of the model across different geographies and environments. The code is publicly available at:
https://github.com/w-tingting/DK-former
.