## 5 Experimental Results
### Experiments with Intelligent Vehicle
#### 5.2.2 Experimental Analysis  
First, we conducted the traffic sign detection at different times. We collected data at 10: 00 a.m. (daytime) and at 19: 00 p.m. (sunset). Figure 19 shows the test site at daytime with different routes. There are approximately more than 500 images, including traffic signs. We use the proposed method and four compared methods to detect these signs. Figure 20 shows some of the results. From the results, we can find that the proposed method can accurately detect the traffic signs. We also divide these signs into three kinds: small sign, medium sign, and large sign. Table 4 shows the comparison results. From the table, we can find that our method has a precision of 93.2%, 90.2%, and 92.8%, in small sign, medium sign, and large sign, respectively. The results have a better performance than SSD, YOLOv2, and YOLOv3. YOLOv5 performs marginally better than our method. This is because YOLOv5 is suitable in the scenarios in which there is plenty of light. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(\mathbf{AP}^{small}\) & \(\mathbf{AP}^{medium}\) & \(\mathbf{AP}^{large}\) & **mAP** \\ \hline SSD & 0.752 & 0.650 & 0.744 & 0.728 \\ YOLOv2 & 0.784 & 0.712 & 0.752 & 0.744 \\ YOLOv3 & 0.808 & 0.796 & 0.832 & 0.806 \\ YOLOv5 & 0.924 & 0.956 & 0.968 & 0.948 \\ Faster-R-CNN\_ours & 0.932 & 0.902 & 0.928 & 0.926 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the comparative experiments at daytime. Figure 17: Image display interface. Figure 18: Detection examples. (a) site 1; (b) site 2. Figure 21 shows the test sites at 19: 00 p. m. (sunset). From the figure, we can find that the sunset light is much lower than the daytime light. From Figure 22, we can find that our method accurately detects the traffic signs. In this test, we also compare our method with the other four state-of-the-art methods. The comparison results are shown in Table 5. From the table, we can find that our method has a precision of 87.4%, 91.8%, and 92.2%, in small sign, medium sign, and large sign, respectively. Compared with the test at daytime, the accuracy decreases marginally. This is because the dim light can affect the traffic sign detection. Compared with the four other methods, these results showcase a better performance. The results show that the proposed method has a great robustness at different times. Figure 19: Test site at daytime: (**a**) city expressway; (**b**) ordinary urban road. Figure 20: Traffic sign detection results: (**a**) city expressway; (**b**) ordinary urban road. The tests at both daytime and sunset are based on a sunny day. To further evaluate the performance of the proposed method, we also conduct the experiment on a rainy day. Considering the limitations of the camera, the rainy day we selected was no characterized by heavy rain, as this may compromise the ability to see the traffic signs clearly. The test sites are shown in Figure 23. From the figure, we can find that the visibility on a rainy day is much lower than the visibility on a sunny day (both at daytime and sunset). Figure 24 shows that the traffic signs are also accurately detected by the proposed method. To further evaluate the proposed method, we also conduct the four state-of-the-art methods, namely SSD, YOLOv2, YOLOv3, and YOLOv5, for comparison. The comparison results are shown in Table 6. From the table, we can find that our method has a precision of 85.3%, 88.4%, and 82.1%, in small sign, medium sign, and large sign, respectively. Although the detection accuracy is marginally lower than the test on a sunny day, the proposed method still performs better than the four other methods. It can be seen that the proposed method displays great robustness on different weather conditions. Figure 23: Test site on rainy day: (**a**) city expressway; (**b**) ordinary urban road.