# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 4 Traffic Sign Detection
### Simplified Gabor Wavelet Model
Furthermore, all the traffic sign edges of the synthesized filtering maps are strengthened in the last image of Figure 4. From the above, we concluded that SGW could really enhance the edges of the images, whilst saving computing time. Through the above analysis, we can clearly see that SGW had better edge enhancement capacity. In the following section, we analyze the filtering effect of SGW for the non-edge regions. The images (a) in Figures 5 and 6 are the RGB images of the traffic scene. The images (b) in Figures 5 and 6 are grayscale images which were converted from the RGB images. The images (c) in Figures 5 and 6 are the synthetic maps. We took one row vector from each of the grayscale images (b) in Figures 5 and 6, as shown in the orange line at the same position, in each of the images (c) in Figures 5 and 6, we took one row vector as shown in the blue line. To make the two vectors visually comparable, the vector from the synthetic maps was transformed by Equation (7). \(V_{1}(i)\) denotes the vector taken as above, \(L\) is the length of the vector \(V_{1}(i)\), and \(V_{2}(x)\) is the vector after transformation:  
\[V_{2}(x)=255-\frac{V_{1}(x)}{\max[|V_{1}(i)|]}\times 255,x=1,\dots,L\;\;i=1, \dots,L \tag{7}\]  
We compared the two vectors in the images (d) of Figures 5 and 6. In the two images, the orange curves represents the vectors obtained from the grayscale map, and the blue curves represents the vectors from the synthesized map. The areas in the red dashed line box corresponds to the areas of the vectors of images (images (b) of Figures 5 and 6, images (c) of Figures 5 and 6) in the same style box. The vector cut by the red dashed line box was the painted area in the traffic sign, which is more uniform when compared with the nature object in the traffic sign scene. In the two figures, we can see that the amplitude of the blue lines in the curves are smaller than that of the orange ones, which means that the noise of the painted image inside the traffic sign is suppressed, and the image was smoothed. In image (d) of Figure 6, the orange curve from the vector of the grayscale map was in a state of decline. The reason for the decline of the vector was an imbalance in illumination, as we could see from the image that the traffic sign was slanted towards the cameras. In image (d) of Figure 6, when compared with the orange curve, the blue curve is balanced, which means that the imbalance illumination was rectified by the SGW filters. Through many experiments, we found that that SGW could not only enhance the edges, but could also smooth the surface of artificial objects, such as painted non-edge areas. This meant that the SGW could reduce noise in the areas of the surface of artificial objects, such as painted areas where the color was uniform. Furthermore, the SGW could also overcome the effect of imbalance illumination. These characteristics of SGW are very important for the next stage of our method. Figure 4: Sample processing by eight SGW filters and output of maximization at each pixel in the eight feature maps: (**a**) \(\omega=0.3\pi\), \(\theta=0\); (**b**) \(\omega=0.3\pi\), \(\theta=\pi j/4\); (**c**) \(\omega=0.3\pi\), \(\theta=\pi j/2\); (**d**) \(\omega=0.3\pi\), \(\theta=3\pi j/4\); (**e**) \(\omega=0.5\pi\), \(\theta=0\) ; (**f**) \(\omega=0.5\pi\), \(\theta=\pi j/4\); (**g**) \(\omega=0.5\pi\), \(\theta=\pi j/2\); (**h**) \(\omega=0.5\pi\), \(\theta=3\pi j/4\); (**i**) the input of the image; (**o**) the output of the synthesized map.