## 5 Experimental Results
### Experiments with Dataset
#### 5.1.2 Experimental Analysis  
In this section, we first compare the improved Faster R-CNN and Faster R-CNN, where the backbone network is Resnet50. The experimental results are presented in Table 2. The mAP values of the original Faster R-CNN and the improved Faster R-CNN are 78.2% and 86.5%, respectively. The mAP was directly improved by 8.3 percentage points, which is a significant improvement. The detection effect of small targets was significantly improved  
Figure 12: Annotation file. by approximately 12 points. The proposed improvement scheme significantly improved the detection effect of small targets. The experiment demonstrates that the improvement achieved in this study is an effective approach, which can comprehensively improve the detection ability of the Faster R-CNN for traffic sign targets. To further illustrate the effectiveness of the three improvement measures proposed by us, the effectiveness of each component of the improved Faster R-CNN was analyzed through various ablation experiments (Table 3). In the experiments, the FPN, improved ROI align, and DCN modules were gradually added. The experimental results of traffic sign targets with different detection difficulties, detection effects, and test speed of single pictures are explained and analyzed. From the abovementioned experimental results, it can be established that the detection effect of the three improved methods proposed in this study for various sized targets of traffic signs was considerably improved. The contrast characteristics of the pyramid FPN ablation experiments may be found to detect small targets, and the medium-target effect will certainly contribute to the small size of the mAP increased by approximately 7%, signifying that using more underlying characteristics for small target detection can improve the effect of detection for small targets, with a medium-size mAP of 2.9%, and a large-scale mAP that does not improve much. The reason is that large-size targets contain a lot of feature information and can be detected and classified. The ablation experiment that improved ROI pooling into ROI align showed a small improvement in all indicators, and the detection network's ability to detect small targets was further strengthened, demonstrating the effectiveness of this method. The ablation experiment of the DCN shows that the mAP was improved by approximately 2.5%, and the detection accuracy of small targets was significantly improved. This is mainly because, as a result of the addition of the DCN, the convolution not only becomes more flexible to check the receptor field of the target shape, but also shows high efficiency, and the learning ability of the Faster R-CNN is enhanced. The detection of small targets was further improved. Moreover, it can be observed from the experiment that the feature pyramid and DCN modules proposed in this study will increase the time of image detection to a certain extent. The detection results of a part of the TT100K test set are presented in Figure 13. The red detection box represents the detected traffic sign target, and the yellow number represents the confidence of the detected traffic sign. As observed from the figure, the improved scheme can achieve an accurate detection of traffic sign targets in various scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(\mathbf{AP}^{small}\) & \(\mathbf{AP}^{medium}\) & \(\mathbf{AP}^{large}\) & **mAP** & **FPS** \\ \hline Faster R-CNN & 0.748 & 0.812 & 0.805 & 0.782 & 9.3 \\ Faster R-CNN\_ours & 0.861 & 0.874 & 0.836 & 0.865 & 8.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the comparative experiments before and after the improvement of the algorithm.