# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Traffic Sign Classification
For the three CNNs, we trained them for 500 epochs, on the GTSRB dataset with a batch size of 100, respectively. Figure 13, from left to right, illustrates the RGB image, grayscale image, the output of the grayscale image filtered by SGW, and the visualization output of the first convolution layers of the CNN. Figure 14 shows the result of the output of the first convolution layers of the method proposed by Reference [41]. Compared with Reference [41], in the first convolutional layers of our CNNs, although the structure of the image was disrupted, similar to the first level of other CNNs, including those in Reference [41], the edge information in our CNNs was more significant. As mentioned above, the edges were the key information of the traffic signs, so this may be one of the reasons why our algorithm was more \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Layers**} & **CNN for Circular** & **CNN for Triangle** & **CNN for Overall Traffic** \\  & **Traffic Signs** & **Traffic Signs** & **Signs** \\ \hline \multirow{2}{*}{CONV\_Lay\_1} & Kernels: 5 \(\times\) 5 \(\times\) 6 \(\times\) 8 & Kernels: 5 \(\times\) 5 \(\times\) 6 \(\times\) 8 & Kernels: 5 \(\times\) 5 \(\times\) 6 \(\times\) 8 \\  & Stride: 1 & Stride: 1 & Stride: 1 \\ \hline ReLU\_Lay\_1 & Rectified Linear Unit & Rectified Linear Unit & Rectified Linear Unit \\ \hline \multirow{2}{*}{POOL\_Lay\_1} & Method: max pooling & Method: max pooling & Method: max pooling \\  & Size: 2 \(\times\) 2 & Size: 2 \(\times\) 2 & Size: 2 \(\times\) 2 \\  & Stride: 2 & Stride: 2 & Stride: 2 \\ \hline \multirow{2}{*}{CONV\_Lay\_2} & Filters: 5 \(\times\) 5 \(\times\) 12 \(\times\) 6 & Filters: 5 \(\times\) 5 \(\times\) 12 \(\times\) 6 & Filters: 5 \(\times\) 5 \(\times\) 12 \(\times\) 6 \\  & Stride: 1 & Stride: 1 & Stride: 1 \\ \hline ReLU\_Lay\_2 & Rectified Linear Unit & Rectified Linear Unit & Rectified Linear Unit \\ \hline \multirow{2}{*}{POOL\_Lay\_2} & Method: max pooling & Method: Max pooling & Method: Max pooling \\  & Size: 2 \(\times\) 2 & Size: 2 \(\times\) 2 & Size: 2 \(\times\) 2 \\  & Stride: 2 & Stride: 2 & Stride: 2 \\ \hline CONV\_Lay\_4 & Filters: 432 \(\times\) 20 & Filters: 432 \(\times\) 15 & Filters: 432 \(\times\) 43 \\ (FC) & Stride: 1 & Stride: 1 & Stride: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Details of the proposed CNNs architecture in our method. effective. This implied that edge enhancement image preprocessing technology was helpful to improve edge-based target recognition, such as traffic signs, fingerprint recognition, iris recognition, etc. The relationship between the loss and trained epochs of the three networks is shown in Figure 15, and the accuracy-epoch curves are shown in Figure 16. The recognition accuracy maintained a substantial increase, and the loss showed an obvious decline as the number of epochs increased due to the training before the first 90 epochs, so accuracy was below 90%. This was because the CNNs had a severe lack of training, so almost all the training samples provided a positive range for the cost function. The increase in the growth of accuracy began to decline between 90 epochs and 230 epochs and was stable between 230 epochs and 430 epochs. After 470 epochs, the recognition rate of the three networks remained at a stable value, and the value of the loss of the three CNNs was close to zero. Therefore, we concluded that under our algorithm and the current training database, the maximum recognition rate was reached after training 470 epochs with a batch size of 100. As shown in Table 7, the accuracies were 99.28%, 99.49%, and 98.70%, for the triangle, circle, and overall classifications, respectively. Our method reached an accuracy of 99.43%, when we classified the two super classes as a whole using the two CNNs. What needs to be mentioned is that the accuracy of the CNN for circular traffic signs obtained the highest value because the amount of circular traffic signs was large, and the number of classes was small.