## 4 Experimental Results
### Ablation Study
#### 4.3.3 Ablation Experiment  
Next, we conducted ablation experiments to study the impact of our proposed method on the performance of the algorithm model. We applied different methods to the baseline model (\"\(\surd\)\" indicates addition), as shown in Table 5.  
We applied our method to the YOLOv8 model, as shown in Figure 4.  
We inserted environmental perception attention (EPA) after each C2f module in the backbone network. Compared with the baseline model, the introduction of long-range spatial information through EPA can effectively enhance the detection ability of the model. After five down-sampling operations in the backbone network, feature maps P1 to P5 were generated. We integrated P2, P3, and P4 feature maps into the neck network after TDSF fusion. The experimental results demonstrate an improvement in performance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Params (M)** & **Flops (G)** \\ \hline Baseline & 59.6 & 34.0 & 11.139 & 28.7 \\ +TSFM (P3–P5) & 59.7 & 34.1 & 11.271 & 29.9 \\ +TSFM (P2–P4) & 59.9 & 34.4 & 11.172 & 30.0 \\ +TSFM (P2–P4)+TSFM (P3–P5) & 60.2 & 34.4 & 11.304 & 31.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments of different layer fusion configurations of the three-scale fusion module.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Method**} & & & & \\ \hline \multirow{2}{*}{**Baseline**} & **EPA** & **TSFM** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Params (M)** & **Flops (G)** & **FPS** \\  & **(k = 9)** & **(P2–P4)** & & & & & \\ \hline \(\surd\) & & & 59.6 & 34.0 & 11.139 & 28.7 & 160 \\ \(\surd\) & \(\surd\) & & 60.2 & 34.5 & 11.160 & 28.8 & 142 \\ \(\surd\) & & \(\surd\) & 59.9 & 34.4 & 11.172 & 30.0 & 153 \\ \(\surd\) & \(\surd\) & \(\surd\) & 60.9 & 34.9 & 11.193 & 30.1 & 134 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results.  
compared to the baseline model, indicating that incorporating low-level feature maps (P2) and aggregating multi-scale feature information effectively enhances the model's performance. Finally, EPA and TSFM were simultaneously applied to the benchmark model, enabling the model to better adapt to diverse traffic scenarios and environmental changes, further boosting its performance. This suggested that the two methods exhibited strong coupling and a certain degree of synergy.  
Although our method can effectively improve model performance, the amount of computation and number of parameters also increases, and the detection speed decreases (Nvidia A800 test FPS). Specifically, the number of parameters and the amount of computation (FLOPs) increased by 0.48% and 4.88%, respectively, and the FPS decreased by 16.25%. In some practical application scenarios (such as mobile or embedded devices), the increased computational complexity may bring performance bottlenecks on some resource-constrained devices. To solve this problem, future research can try to reduce the amount of computation through methods such as model pruning [55] quantization, and distillation [56], while maintaining high accuracy and speed. The amount of computation of the model can also be further reduced through lightweight methods [39], or the model architecture can be adjusted according to the resource conditions of different hardware platforms, and the inference speed can be improved through hardware acceleration (such as GPU, TPU), so as to find the best balance between performance and resource requirements.  
In addition, we conducted experiments on the KITTI dataset [57] in the field of autonomous driving to further explore the advantages of the proposed method. The experimental results are shown in Table 6. Compared with the baseline, our method is still effective on other datasets and effectively improves the detection performance.  
Figure 4: Applying our method to the YOLOv8.