Traffic Sign Recognition with Vision Transformers

Traffic sign recognition is an integral part of future autonomous driving systems. Deep learning has been applied in this task, while the performance of the recent vision Transformers is unexplored. In this study, eight different vision Transformers are validated in three real-world traffic sign datasets for the first time. The experimental results demonstrate that the best vision Transformer has a performance between the pre-trained DenseNet and the DenseNet trained from scratch. Besides, the best vision Transformers model has less training time compared to DenseNet.