# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 4 Experiments and Discussion
### Analysis of Ablation Experiments
#### 4.3.5 Evaluation of the Attention Mechanism Module's Efficacy  
To further verify the validity of the adding position of the attention mechanism as well as the adding method, and to obtain the optimal adding result, this paper conducts relevant experiments on the adding position of the SE attention mechanism.  
Table 7 displays the comparison findings of the experiments. Among them, YOLOv5s+_K_HSPPF represents the network model after the improvement described above; Y_C3 SE_B, Y_C3 SE_N, and Y_C3 SE_H symbolize the incorporation of the SE attention mechanism into the backbone network's final C3 module, the C3 module of the Neck part, and the C3 module ahead of the model's detection header, respectively, based on the improvement in the previous three subsections; and the fusion of the SE attention mechanism in the final C3 module of the backbone network and the C3 module of the Neck portion is represented by Y_SE_B_SE_N, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **FPS (Hz)** \\ \hline YOLOv5s+ & 92.71 & 87.72 & 92.95 & 75.32 & 97 \\ YOLOv5s+\_K & 92.69 & 87.93 & 93.13 & 75.29 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental comparison before and after the addition of the H-SPPF module.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Neck** & **Head** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** \\ \hline YOLOv5s+\_K_HSPPF & \(\times\) & \(\times\) & \(\times\) & 92.69 & 87.93 & 93.13 \\ Y\_C3 SE\_B & \(\surd\) & \(\times\) & \(\times\) & 91.75 & 88.19 & 92.39 \\ Y\_C3 SE\_N & \(\times\) & \(\surd\) & \(\times\) & 92.53 & 87.52 & 92.18 \\ Y\_C3 SE\_H & \(\times\) & \(\times\) & \(\surd\) & 91.56 & 88.26 & 92.33 \\ Y\_SE\_B\_SE\_N & \(\surd\) & \(\surd\) & \(\times\) & 91.72 & 91.77 & 93.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparing the effects of adding the module for the attention mechanism at various locations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **FPS (Hz)** \\ \hline YOLOv5s+\_K & 91.43 & 87.6 & 92.65 & 75.25 & 97 \\ YOLOv5s+\_K & 92.71 & 87.72 & 92.95 & 75.32 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experiments before and after improving the clustering algorithm.  
Indicating whether the attention mechanism module is fused in the appropriate model portion or not, \"\(\times\)\" and \"\(\vee\)\", respectively, denote the state of the attention mechanism module.  
Table 7 illustrates that the final fusion procedure yields the best outcomes, fusing the SE attention mechanism in the backbone network and Neck part. Specifically, after fusion in the backbone network, the Neck portion, and the C3 module prior to the detecting head, the average accuracy drops rather than rises. In contrast, in this paper, the last C3 module of the backbone network and the C3 module of the neck part of the recall rate and average accuracy obtained from the [EMAIL_ADDRESS] trials--91.77% and 93.88%, respectively--are where the SE attention mechanism is fused (the table in black bold font reveals); both metrics are improved over the model before adding the attention mechanism, by 3.84% and 0.75%, respectively.  
Therefore, the ability of the model to recognize object features can be strengthened by combining the SE attention mechanism in the Neck component and the backbone network, respectively, utilizing the channel attention. This enhances the model's ability to extract the road traffic sign objects. Consequently, it demonstrates how the attention techniques proposed in this research can be combined to further improve the model's performance.