With the development of the transportation industry, traffic signs detection and recognition had become one of the most popular research directions in advanced driving assistant system. In the actual driving road, traffic signs have the characteristics of small targets and various categories. In view of the problem that the existing detection and recognition algorithms are difficult to take into account the accuracy and speed at the same time, a detection and recognition method combining YOLOv5l and vision transformer（ViT) was proposed. Firstly, YOLOv5l was used to detect objects to obtain the location information of traffic signs. Then, the detection results were input into ViT for recognition, and the DenseNet network was added to the feature connection unit in order to densely connect the original features and the convolutional feature maps, thus enhancing the transferability of features and improving the recognition accuracy. The results show that this algorithm has better performance on GTSDB and GTSRB datasets. The detection rate of traffic signs reaches 200 ms, and the accuracy rate reaches 98.78%, which is about 4% higher than that of the fully connected network.