Recognition of traffic signs is a crucial aspect of self-driving cars and
driver assistance systems, and machine vision tasks such as traffic sign
recognition have gained significant attention. CNNs have been frequently used
in machine vision, but introducing vision transformers has provided an
alternative approach to global feature learning. This paper proposes a new
novel model that blends the advantages of both convolutional and
transformer-based networks for traffic sign recognition. The proposed model
includes convolutional blocks for capturing local correlations and
transformer-based blocks for learning global dependencies. Additionally, a
locality module is incorporated to enhance local perception. The performance of
the suggested model is evaluated on the Persian Traffic Sign Dataset and German
Traffic Sign Recognition Benchmark and compared with SOTA convolutional and
transformer-based models. The experimental evaluations demonstrate that the
hybrid network with the locality module outperforms pure transformer-based
models and some of the best convolutional networks in accuracy. Specifically,
our proposed final model reached 99.66% accuracy in the German traffic sign
recognition benchmark and 99.8% in the Persian traffic sign dataset, higher
than the best convolutional models. Moreover, it outperforms existing CNNs and
ViTs while maintaining fast inference speed. Consequently, the proposed model
proves to be significantly faster and more suitable for real-world
applications.

通过结合卷积网络和 Transformer 网络的优势，本研究提出了一种新型的模型用于交通标志识别，实验证明该模型在德国和波斯交通标志数据集上超过了纯 Transformer 模型和最佳卷积网络的准确率，并且在保持快速推理速度的同时，更适合实际应用。

基于局部视觉变换器的交通标志识别

Traffic Sign Recognition Using Local Vision Transformer

We introduce the first Neural Architecture Search (NAS) method to find a
better transformer architecture for image recognition. Recently, transformers
without CNN-based backbones are found to achieve impressive performance for
image recognition. However, the transformer is designed for NLP tasks and thus
could be sub-optimal when directly used for image recognition. In order to
improve the visual representation ability for transformers, we propose a new
search space and searching algorithm. Specifically, we introduce a locality
module that models the local correlations in images explicitly with fewer
computational cost. With the locality module, our search space is defined to
let the search algorithm freely trade off between global and local information
as well as optimizing the low-level design choice in each module. To tackle the
problem caused by huge search space, a hierarchical neural architecture search
method is proposed to search the optimal vision transformer from two levels
separately with the evolutionary algorithm. Extensive experiments on the
ImageNet dataset demonstrate that our method can find more discriminative and
efficient transformer variants than the ResNet family (e.g., ResNet101) and the
baseline ViT for image classification.

通过局部模块来提高图像转换器的视觉表示能力，建立搜索空间和搜索算法体系，提出分层神经架构搜索方法并证明其在 ImageNet 数据集上对于图像分类比 ResNet 和 ViT 机型更优。