# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 4 Experiment
### Quantitative Results  
To validate the detection performance of the proposed method, we conducted extensive experiments on both the DZGSet and DAIR-V2X datasets. Our network was compared against several benchmarks, including single-vehicle perception (baseline), early and late cooperative perception, and state-of-the-art intermediate cooperative perception methods. As summarized in Table 2, the proposed method consistently achieved the highest AP, underscoring its superiority in vehicle-infrastructure cooperative perception. Specifically, the proposed method achieved an [EMAIL_ADDRESS] of 0.652 and an [EMAIL_ADDRESS] of 0.493 on the DZGSet dataset, and an [EMAIL_ADDRESS] of 0.780 and an [EMAIL_ADDRESS] of 0.632 on the DAIR-V2X dataset. When compared to the single-vehicle perception method (PointPillars), our approach demonstrated a substantial improvement, with an [EMAIL_ADDRESS] increase of 0.288 on the DZGSet and 0.299 on the DAIR-V2X datasets. Clearly, the [EMAIL_ADDRESS] and [EMAIL_ADDRESS] values on the DAIR-V2X dataset are generally higher than those on the DZGSet dataset. This indicates that the larger-scale DAIR-V2X dataset offers a more diverse array of training samples, enabling the model to learn more robust features and enhance detection accuracy, while the DZGSet is smaller in scale, its multi-category annotations create a more complex learning environment, allowing for a broader evaluation of the model's generalization capabilities. This complexity ultimately improves the model's adaptability to various detection scenarios. Furthermore, against early, late, and state-of-the-art intermediatecooperative perception methods, our method achieves minimum improvements of an [EMAIL_ADDRESS] increase of 0.037 (F-Cooper) and an [EMAIL_ADDRESS] increase of 0.043 (F-Cooper) on the DZGSet dataset, as well as an [EMAIL_ADDRESS] increase of 0.034 (Coalign) and an [EMAIL_ADDRESS] increase of 0.028 (Coalign) on the DAIR-V2X dataset. The single-vehicle perception method relies solely on the vehicle's sensors and does not utilize supplementary infrastructure information, leading to significantly lower performance compared to the proposed method. On the other hand, intermediate cooperative perception methods typically fuse vehicle and infrastructure features at a single scale through concatenation or weighting mechanisms, which is insufficient for optimal detection performance, and therefore inferior to the proposed method as well. The advancements of our vehicle-infrastructure cooperative perception network are primarily attributed to the Multi-Scale Dynamic Feature Fusion Module, which effectively integrates features from both domains. Specifically, at smaller scales, it refines bounding box localization by fusing spatial details, while at larger scales, it aggregates semantic information to mitigate spatial misalignment between vehicle and infrastructure sensors. By dynamically capturing correlations between vehicle and infrastructure features in both channel and spatial dimensions, it ensures thorough fusion of interacted features and the original vehicle-infrastructure features at each spatial location. This synergy allows the proposed approach to fully exploit the complementary perception data from infrastructure, achieving state-of-the-art detection accuracy while maintaining robustness in complex real-world scenarios. Furthermore, we performed inference time experiments on the DAIR-V2X dataset. The analysis reveals a trade-off between computational efficiency and detection performance. The single-vehicle perception method achieves the fastest inference time of 28.27 ms, owing to its low model complexity. In contrast, V2X-ViT has the slowest inference time at 172.01 ms, primarily due to the high computational demands of its attention mechanisms. The proposed method records an inference time of 57.62 ms, achieving a balance between accuracy and computational cost.