# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 4 Traffic Sign Recognition Experiment
### Traffic Sign Recognition Experiment  
#### 4.2.1 Traffic Sign Dataset  
This paper uses the German Traffic Sign Recognition Benchmark (GTSRB), which was presented at the 2011 International Joint Conference on Neural Networks (IJCNN). The internal traffic signs are collected from the real road traffic environment in Germany, and it has become a common traffic sign dataset used by experts and scholars in computer vision, self-driving and other fields. The GTSRB comprises 51,839 images, which are divided into training and testing sets. A total of 39,209 and 12,630 images are provided in the training and testing sets, accounting for approximately 75% and 25% of the whole, respectively. Each image contains only one traffic sign, which is not necessarily located in the center of the image. The image size is unequal; the maximum and smallest images are \(250\times 250\) and \(15\times 15\) pixels, respectively [37; 38].  
The traffic sign images in GTSRB are taken from the video captured by the vehicle-mounted camera. As shown in Figure 7, GTSRB includes 43 classes of traffic signs, and the number of different types of traffic signs varies. Each type of traffic sign corresponds to a catalogue, which contains a CSV file annotated with a class label and a single image of multiple tracks (each track includes 30 images). In accordance with the different instruction contents, GTSRB can also be divided into six categories: speed limit, danger, mandatory, prohibitory, derestriction and unique traffic signs as shown in Figure 8. The same type of traffic signs include different resolutions, illumination conditions, weather conditions, occlusion degree, tilt levels and other images, making the dataset more in line with the actual road scenes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Number** & **Type** & **Feature Map** & **Convolutional** & **Feature** & **Neuron** \\  & **Type** & **Number** & **Kernel Size** & **Map Size** & **Number** \\ \hline
1 & Convolutional Layer & 6 & \(5\times 5\) & \(28\times 28\) & 4704 \\
2 & Pooling Layer & 6 & \(2\times 2\) & \(14\times 14\) & 1176 \\
3 & Convolutional Layer & 12 & \(5\times 5\) & \(10\times 10\) & 1200 \\
4 & Pooling Layer & 12 & \(2\times 2\) & \(5\times 5\) & 300 \\
5 & Fully-connected Layer & 120 & \(1\times 1\) & \(1\times 1\) & 120 \\
6 & Fully-connected Layer & 84 & \(1\times 1\) & \(1\times 1\) & 84 \\
7 & Output Layer & 43 & - & - & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter settings of the improved LeNet-5 network model.  
After image preprocessing, an artificial dataset must be generated for GTSRB. Given that the number of different types of traffic signs in GTSRB varies, this condition easily causes the imbalance of sample data. Different types of traffic signs have evident differences during classification and recognition, which affect the generalization of the entire network model. Generating an artificial dataset aims to construct a new artificial sample by randomly sampling from the value space of each attribute feature of the same sample type. Therefore, the number of different kinds of traffic signs is as equal as possible to solve the problem of sample data imbalance. After generating the artificial dataset, the 43 classes of traffic signs are shown in Figure 9.  
Figure 8: Six categories of traffic signs sample images.  
Figure 7: The number of 43 classes of traffic signs.