## 4 Experiments
### Ablation Experiment
#### 4.3.2 Replacement of the Original Concat Module with the FA-Concat Module Only  
As indicated in Table 5, incorporating the FA-Concat module improved the model's mAP@80 by 4.99% and mAP@75-95 by 4.27%, demonstrating that the enhanced feature fusion strategy substantially strengthened the model's object recognition capability, particularly under high-precision matching conditions. However, this improvement came at the cost of a significant increase in computational complexity, resulting in a marked reduction in inference speed (fps). Further evidence from Table 6 shows that, in the speed measurement task, the FA-Concat module outperformed the original YOLOv11 model in both speed estimation accuracy and stability, indicating that its enhanced feature representation capacity contributes to better capturing subtle variations in the target's motion trajectory, thereby improving the consistency and reliability of velocity estimation. These findings confirm that the improved FA-Concat module significantly enhances detection accuracy by employing an aligned feature fusion strategy. Unlike the original simple concatenation approach, the FA-Concat module effectively aligns features from different network layers to better capture fine-grained target details. Nevertheless, this enhancement also increases network complexity, substantially impacting computational efficiency. Specifically, the FA-Concat module introduces convolutional chains with residual connections to mitigate differences between hierarchical features, optimizing feature integrity, especially when fusing low-resolution semantic features with high-resolution detail features. Furthermore, the module customizes the number of convolutional layers based on the receptive field variations across feature levels to further strengthen the fusion effect. Despite achieving notable improvements in feature fusion, the considerable increase in computational overhead leads to a significant drop in fps. 3.3 Simultaneous Replacement of the C3k2 and Concat Modules with the Ghost Conv and FA-Concat Modules  
As illustrated in Table 6, when both the Ghost Conv and FA-Concat modules are integrated, a significant improvement in the stability of speed estimation can be observed. Compared to the original YOLOv11 model, the error variance decreased by 31.07% under the 30-frame video setting and by 4.62% under the 60-frame setting, indicating that the proposed architectural combination plays a positive role in enhancing model robustness. Nevertheless, it is important to note that the overall speed estimation accuracy under the 60-frame condition dropped by 0.25%. This phenomenon suggests that the increased model complexity results in reduced inference efficiency at higher frame rates, thereby impairing the model's responsiveness to subtle displacements in rapid successive frames and exposing a certain performance bottleneck. From the detection performance results in Table 5, compared to introducing only the FA-Concat module, the mAP@80 increased by 1.86%, whereas the mAP@75-95 droppedby 0.52%. This outcome further verifies that, under stricter detection accuracy requirements, the Ghost Conv module exhibits limitations in feature extraction depth and richness. Although the FA-Concat module enhances feature representation consistency and completeness through cross-layer alignment, the semantic information loss introduced by the Ghost Conv module at the low-level feature extraction stage constrains the overall detection network's performance ceiling. Hence, in high-precision object detection tasks, the trade-off undertaken by the Ghost Conv module between computational efficiency and representational power may no longer be optimal. Meanwhile, the FA-Concat module still presents opportunities for further improvement in feature alignment and fusion strategies.