# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 4 Traffic Sign Detection
### Traffic Sign Proposal Extraction  
To detect the traffic signs in the traffic scene, in the following stage, we aimed to find the traffic sign proposal regions proposed by the MSERs region detector. The MSERs algorithm has often been used as a method for blob detection in images. This technique was proposed by [PERSON] [32] to solve problems regarding the robust wide-baseline stereo, and was first used for traffic sign detection in Reference [33]. MSERs are regions that maintain their shape, whilst thresholding the image at several  
Figure 5: (a) Traffic sign of RGB; (b) grayscale image; (c) synthetic Gabor filtered map; (d) two vector contrast graphs. Figure 6: (a) Traffic sign of RGB; (b) grayscale image; (c) synthetic Gabor filtered map; (d) two vector contrast graphs. levels. The advantage of this approach is its robustness to variations in contrast and lighting conditions in outdoor traffic scenes. This method has been widely used in natural scene text location and traffic sign detection. Reference [33] used MSERs to detect traffic signs on a grayscale map. In Reference [10], MSERs were used to detect traffic signs with a region proposal from the color probability map, which increased the contrast between the target and background. Unlike References [10; 33], we extracted the maximally stable extremal regions from the synthetic simplified Gabor filtered map. To find the MSERs, the value of each pixel in the synthetic simplified Gabor filtered map was set to 1 if its intensity was larger than a threshold; otherwise, it was set to 0. When the SGW map was threshold at several levels, the most stable connected components that maintained their shapes were defined as MSERs. As mentioned above, after being filtered by SGW, the edges of the traffic signs were strengthened and the area inside the traffic sign was smoothed. According to the algorithm principles of MSERs, this could help the regional proposal of traffic signs to be obtained. The background of natural scenes is complex, and the MSERs detector will detect a large number of areas which are not traffic signs, as shown in image (d) and image (e) of Figure 7. It is necessary to design filter rules to reduce the number of candidates. From this approach, the processing time of the system can be greatly reduced, and accuracy can be improved. In our methods, the features used to filter out the candidates included the following: The minimum and maximum height of the proposed areas, the minimum and maximum width of the proposed areas, the aspect ratio, and the duty ratio. The aspect ratio was defined as the width of the proposed area divided by the height of the proposed area. The duty ratio was defined as the MSERs area divided by the bounding box area. The bounding box was the smallest external rectangle of the traffic sign. The calculation of the duty ratio is shown as Equation (8). When the pixel \(I(x,y)\) was in the MSERs, the value of \(f(x,y)\) was 1; otherwise, it was 0. The width and height in the formula refer to the width and height of the bounding box, respectively. The numerical ranges of these parameters in the filter rules are shown in Table 2. The numerical ranges of the parameters in Table 2, were selected by empirical data in the experiment. \[\text{M}_{B}=\frac{\sum\limits_{x=1}^{width\;height}f(x,y)}{width\times height} \tag{8}\]  
Figure 7 shows examples of the traffic scene images processed by SGW and MSERs, filtered by the rules we designed. Image (a) and image (b) of Figure 7 are the RGB and grayscale images of the traffic scene. Image (c) of Figure 7 is the synthesized SGW map, with the maximum values of the eight filtered map at each pixel. From image (c) of Figure 7, we can clearly see that the edges and the inside symbol of the traffic signs were enhanced. Image (d) and image (e) of Figure 7 are the grayscale image and synthesized SGW map after being processed by MSERs.