# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Traffic Sign Detection  
To evaluate the performance of our region proposal method, we used the recall rate and false negative (FN) to analyze the performance of our algorithm. The recall rate was defined by Equation (9), and FN refers to the positive samples with a negative predictive value:  
\[Recall=\frac{true\ positives\ detected}{total\ true\ positives}\times 100\% \tag{9}\]  
Table 3 illuminates the traffic proposal extraction results on the GTSDB and CTSD test sets. As we can see from the table, with the MSERs method on the SGW map, there were only one and two traffic signs that had not been proposed in the GTSDB and CTSD, respectively. The traffic sign proposal ability of our method was better than that in Reference [10], and the method of MSERs on the gray-scale image. After image preprocessing by the SGW, the average number of proposals was significantly lower than the other two methods. What needs to be mentioned is that using the defined filter rules, the average number of proposals was further reduced without a decline in the recall rate. A high recall rate and a small number of traffic sign proposals were very important for the fast and high accuracy of the super class classification of the next phase. In this stage, we used SVM to classify the shape that the ROIs belonged to and identified the super-class of the candidate area and tried to classify the shape of the areas segmented from the SGW map and filtered by the defined filter rules from Table 2.  
First, we focused on the size of the training samples and the selection of HOG parameters. To verify the classification effect under different parameters, the dataset was normalized to five different sizes. The HOG specific parameters of the five different sizes are shown in Table 4. The training samples of different sizes were extracted according to their respective parameters, and then sent into the linear SVM training to obtain the detection classifier, where the selection of parameters was the same. The five HOG features were used to train the classifier, to test the performance on the GTSDB and CTSD datasets, as shown in Table 3. From Tables 4 and 5, we can draw the conclusion that an increase in the sample size and feature dimensions increased the training time. Although HOG1, HOG4, and HOG5, had faster detection times than HOG2, the detection accuracy of HOG2 was higher than the other four HOG features. To ensure a high detection rate, HOG2 was chosen as the feature of detection. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **GTSDB** & **CTSD** \\ \hline \multirow{4}{*}{Paper [10]} & Average number of proposals & 325 & 200 \\  & Recall, FNs & 99.63\%, 1 & 99.44\%, 3 \\  & Time (ms) & 67 & 90 \\ \hline \multirow{4}{*}{Grayscale + MSERs} & Average number of proposals & 388 & 321 \\  & Filtered by rules & 118 & 99 \\  & Recall, FNs & 97.1\%, 8 & 98.12\%, 10 \\  & Time (ms) & 40 & 38 \\ \hline \multirow{4}{*}{SGW Map + MSERs} & Average number of proposals & 276 & 178 \\  & Filtered by rules & 83 & 56 \\ \cline{1-1}  & Recall, FNs & 99.63\%, 1 & 99.62\%, 2 \\ \cline{1-1}  & Time (ms) & 46 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the recall, false negatives (FNs) between grayscale + MSERs, SGW Map + MSERs, and Reference [10]. The two precision recall curves from the datasets of GSTDB and CTSD are shown in Figure 12. Precision was defined by Equation (10). As shown in the figure, with the same precision, the recall from GTSDB was better than that from CTSD; in addition, the AUC (area under curve) of the GTSDB was better than that of CTSD, probably because the quality of the CTSD's pictures were not as good as that of GTSDB. The images in the CSTD were of different sizes, and some were taken by camera from the driver's cab across the windshield, so the reflection of the windshield had a negative effect on the quality of the picture.