# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 4 Experiment
### Qualitative Results  
To intuitively illustrate the perception capability of different models, we present detection visualization comparisons in typical scenarios in Figures 6 and 7.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cooperation**} & \multirow{2}{*}{**Cooperation Type**} & \multicolumn{2}{c}{**DZGSet**} & \multicolumn{2}{c}{**DAIR-V2X**} \\ \cline{3-8}  & & & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **
\begin{tabular}{c} **Inference** \\ **Time (ms)** \\ \end{tabular} \\ \hline PointPillars [4] & Independent & None & 0.364 & - & 0.481 & - & **28.27** \\ EarlyFusion [18] & Cooperative & Early & 0.513 & - & 0.626 & - & 67.14 \\ LateFusion [18] & Cooperative & Late & 0.443 & - & 0.561 & - & 34.43 \\ F-Cooper [14] & Cooperative & Intermediate & 0.615 & 0.450 & 0.734 & 0.559 & - \\ V2 VNet [31] & Cooperative & Intermediate & 0.575 & 0.401 & 0.664 & 0.402 & 70.60 \\ OPV2V [13] & Cooperative & Intermediate & 0.602 & 0.431 & 0.733 & 0.553 & 50.97 \\ V2X-ViT [23] & Cooperative & Intermediate & 0.591 & 0.437 & 0.704 & 0.531 & 172.01 \\ Coalign [32] & Cooperative & Intermediate & 0.606 & 0.440 & 0.746 & 0.604 & - \\ The proposed & Cooperative & Intermediate & **0.652** & **0.493** & **0.780** & **0.632** & 57.62 \\ \hline \hline \end{tabular} The bold font indicates the optimal value in the corresponding metric.  
\end{table}
Table 2: Detection performance on the DZGSet dataset and the DAIR-V2X dataset.  
Comparison with single-vehicle perception method (Figure 6): Figure 5(a) highlights the limitations of single-vehicle perception in occlusion and long-range scenarios. For instance, the cyclist marked by the blue circle is occluded by a nearby car, rendering it undetectable for the ego vehicle. Additionally, two pedestrians marked by yellow circles are located at a significant distance, leading to missed or misaligned detections. In contrast, as shown in Figure 5(b), the proposed method successfully detects these objects by leveraging complementary information from infrastructure LiDAR. This demonstrates that in a vehicle-infrastructure cooperation system, infrastructure sensors enhance single-vehicle perception capabilities by providing complementary viewpoint data, effectively mitigating blind spots caused by occlusion and reducing performance degradation typically observed in single-vehicle perception at extended distances.  
Comparison with advanced cooperative perception methods (Figure 7): Figure 5(a)-c visualize the detection results of various cooperative methods (V2 VNet, V2X-ViT, and CoAlign). The yellow circles indicate inaccurate detections, including false positives for pedestrians and cyclists, as well as misaligned bounding boxes for cars. In comparison, the proposed method produces predictions that are closely aligned with the ground truth, as shown in Figure 5(d). The superiority of the proposed method arises from its ability to process vehicle and infrastructure features through the MSDI and PPSA modules at multiple scales. It effectively aggregates spatial details and semantic information from both vehicle and infrastructure features, resulting in more accurate and stable detection outcomes.  
Figure 6: Detection visualization comparison with single-vehicle perception method on the DZGSet dataset. Green boxes denote ground-truth, while red boxes represent detection results. The object marked by the blue circle represents an occluded object and the objects marked by the yellow circle indicate distant objects in the single-vehicle perception.  
Overall, qualitative results highlight that the proposed approach achieved more accurate detection results compared to single-vehicle perception method and state-of-the-art cooperative perception methods.