This research introduces a novel pyramid EATFormer architecture that leverages Vision Transformers and Evolutionary Algorithms to significantly improve traffic sign recognition accuracy and efficiency.
This paper highlights the potential of Convolutional Neural Networks (CNNs) in achieving high accuracy in traffic sign recognition, emphasizing the impact on road safety and autonomous driving.