# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Traffic Sign Classification  
In the detection stage, the traffic signs were classified into two super classes: Circular and triangular traffic signs. In the classification stage, we trained three CNNs for two classification \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Size** & **Cell** & **Block** & **Stride** & **Bin** & **Gradient Direction** & **Dimension** \\ \hline HOG1 & 24 \(\times\) 24 & 6 \(\times\) 6 & 12 \(\times\) 12 & 6 \(\times\) 6 & 9 & \((0,2\pi)\) & 324 \\ HOG2 & 36 \(\times\) 36 & 6 \(\times\) 6 & 12 \(\times\) 12 & 6 \(\times\) 6 & 9 & \((0,2\pi)\) & 900 \\ HOG3 & 42 \(\times\) 42 & 6 \(\times\) 6 & 12 \(\times\) 12 & 6 \(\times\) 6 & 9 & \((0,2\pi)\) & 1296 \\ HOG4 & 56 \(\times\) 56 & 6 \(\times\) 6 & 12 \(\times\) 12 & 6 \(\times\) 6 & 9 & \((0,2\pi)\) & 1296 \\ HOG5 & 64 \(\times\) 64 & 6 \(\times\) 6 & 12 \(\times\) 12 & 6 \(\times\) 6 & 9 & \((0,2\pi)\) & 1764 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different parameters of the Histogram of oriented gradient (HOG) features selected in our method. Figure 12: Detection precision recall curves on the GTSDB and CTSD. The y-coordinate was adjusted for clarity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Method** & **Detection Rate/\%** & **Average Detection Time/MS** \\ \hline  & HOG1 + SVM & 95.88 & 69 \\  & HOG2 + SVM & 99.33 & 93 \\ GTSDB & HOG3 + SVM & 95.49 & 101 \\  & HOG4 + SVM & 83.25 & 71 \\  & HOG5 + SVM & 82.26 & 89 \\ \hline  & HOG1 + SVM & 94.63 & 62 \\  & HOG2 + SVM & 97.96 & 79 \\ CTSD & HOG3 + SVM & 94.08 & 95 \\  & HOG4 + SVM & 81.86 & 65 \\  & HOG5 + SVM & 81.03 & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the support vector machine (SVM) classification with different HOG features. methods. One method trained two CNNs for circular and triangular traffic signs independently. The other method trained one CNN for the overall traffic sign classification. Each of the three CNNs had two convolutional layers, and each of the convolutional layers were followed by a subsampling layer. They all used a fully connected layer to produce the final classification result. The eight Gabor features of each traffic sign were used as inputs of the three CNNs, with a fixed size of 32 \(\times\) 32. The first convolutional layer extracted six features for each input with 8 \(\times\) 6 kernels (size 5 \(\times\) 5). Additionally, the second convolutional layer extracted 12 features for each input; hence, the second convolutional layer consisted of 6 \(\times\) 12 kernels with a size of 5 \(\times\) 5. The 12 feature maps from the second layer were used as feature vector inputs to the fully connected layer, to produce the final classification result. The detailed structures of the three CNNs are shown in Table 6.  
The input of the neural network needs to be a fixed size, but during data gathering of the traffic signs, the image sizes of the GTSRB varied from \"15 \(\times\) 15\" to \"223 \(\times\) 193\", for the varying distances and angles between the camera mounted on a vehicle and the traffic sign. To solve this problem, we resized the images of the dataset to \"32 \(\times\) 32\", before convoluting them using the simplified Gabor filters. In this way, it was ensured that the input of the CNN was fixed to \"32 \(\times\) 32\". To show the performance of our classification method, we tested the three structures of the CNNs on the dataset of the GTSRB. For training and evaluation, the GTSRB dataset was separated into training datasets and test datasets. For triangular traffic sign recognition in the CNN, we used 8970 images for training and 2790 images for testing, with a total of 15 traffic sign classes. For circular traffic sign recognition in the CNN, we used 22,949 images for training and 7440 images for testing, with a total of 20 traffic sign classes. Finally, for overall traffic recognition in the CNN, we used 39,209 images for training and 12,630 images for testing.