# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 4 Traffic Sign Recognition Experiment
### Performance Comparison of Recognition Algorithms  
The proposed algorithm is compared with other algorithms adopted in other literature to verify the performance of traffic sign recognition algorithms. Table 4 lists the comparison of statistics in algorithm performance based on the GTSRB dataset. In the performance comparison experiment, the proposed algorithm and other literature all conducted relevant traffic sign recognition test experiments based on the GTSRB dataset. In reference [39], a traffic sign extraction method based on oriented gradient maps and the Karhunen-Loeve transform was adopted, which achieved good test results by reducing the number of attributes and combining multilayer perceptron. Compared with other algorithms, although the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Method** & \begin{tabular}{c} **Accurate** \\ **Recognition Rate** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Processing Time** \\ **(ms)/Frame** \\ \end{tabular} &
\begin{tabular}{c} **System Environment** \\ \end{tabular} \\ \hline
1 & Multilayer Perceptron [39] & 95.90 & 5.4 & Intel Core i5 processor, 4 GB RAM \\
2 & INNLP + INNC [40] & 98.53 & 47 & Quad-Core AMD Opteron 8360 \\
3 & GF+HE+HOG+PCA [41] & 98.54 & 22 & Intel Core i5 processor @2.50 GHz, 4 GB RAM \\
4 & Weighted Multi-CNN [42] & 99.59 & 25 & NVIDIA GeForce GTX 1050 Ti GPU, Intel i5 CPU \\ Ours & Proposed Method & 99.75 & 5.4 & Intel(R) Core(TM) i5-6500 CPU @3.20 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of statistics in algorithm performance based on the GTSRB dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequence** & **Traffic Signs** & **Test Images** & **TP** & **FN** & \
\begin{tabular}{c} **Accurate** \\ **Recognition** \\ **Rate (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Processing Time** \\ **(ms)/Frame** \\ \end{tabular} \\ \hline
1 & Speed Limit & 1000 & 997 & 3 & 99.70 & 5.4 \\
2 & Danger & 1000 & 999 & 1 & 99.90 & 5.8 \\
3 & Mandatory & 1000 & 997 & 3 & 99.70 & 5.2 \\
4 & Prohibitory & 1000 & 998 & 2 & 99.80 & 4.9 \\
5 & Derestriction & 1000 & 994 & 6 & 99.40 & 6.4 \\
6 & Unique & 1000 & 1000 & 0 & 100.00 & 4.7 \\ Total & - & 6000 & 5985 & 15 & 99.75 & 5.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The classification and recognition test results of six categories of traffic signs. average processing time of this algorithm was relatively short, the accurate recognition rate was the lowest. Therefore, this algorithm is more likely to cause false or missed recognition in the actual road scenes than other algorithms. In reference [40], iterative nearest neighbors-based linear projection was combined with iterative nearest-neighbor classifier. Multiple HOG features were used for detection, and sparse representations were adopted for classification, thereby achieving good recognition performance. Compared with literature [39], although the accurate recognition rate was considerably improved, the average processing time was excessively long, and real-time performance was poor when applied to actual road scenes. In reference [41], a traffic sign recognition method based on the histogram of oriented gradients was utilized. By combining Gaussian filter and histogram equalization for effective image preprocessing, using principal component analysis for dimensionality reduction, and a good classification accuracy was achieved by using a kernel extreme learning machine (K-ELM) classifier, while the average processing time was also further shortened. In reference [42], the weighted multi-CNN was trained by a new training method, and good recognition accuracy was obtained. Although the running environment of the algorithm included GPU and CPU, the average processing time was still relatively long. Deep learning-based methodologies can still be further improved because of the complex structure of the training model, the large amount of calculation, the long training time and the poor real-time performance.