# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments
### Multi-Source Data Fusion Experiment  
This paper's multi-source data fusion model training process focuses on three key aspects. First, to address the challenge of poor detection accuracy for small vessels with significant scale variations in complex nearshore backgrounds, we adjusted the feature weights between channels. Second, recognizing the inherent latency in AIS data transmission, we used a trajectory prediction model to forecast AIS data, enabling synchronization that facilitates matching with vessels in real-time video data. Finally, we proposed a fusion method based on region set partitioning. By dividing the visible range of visual detection and the pixel coordinate regions of the video monitoring system, we performed feature matching between AIS data collected via the AIS receiver and visual information captured through the camera. Additionally, we conducted spatiotemporal consistency checks by comparing pixel coordinates with the geographic locations of AIS trajectories. * Ship Target Detection and Tracking During the training of the model-driven visual detection model, the number of iterations was set to 300, with an input batch size of 16 and an initial learning rate of 0.001. The experimental results are shown in Table 3. The experimental results demonstrate that the enhancements made to the one-stage object detection algorithm significantly improved the model's performance. Following the introduction of the SPD-Conv module, the model's performance for small object detection was notably enhanced, achieving a [EMAIL_ADDRESS] of 0.956 for small objects in the Unknown category. The addition of the Detect_SE module further optimized feature extraction, resulting in a slight increase in the model's [EMAIL_ADDRESS] to 0.976, with improvements across all detection categories. The incorporation of the Focal-EloU loss function effectively boosted the model's detection capabilities for overlapping targets, particularly increasing the detection accuracy for the Unknown category from 0.949 to 0.963, while the overall [EMAIL_ADDRESS] rose to 0.978. Additionally, after integrating the RReLU activation function, the model achieved its optimal performance, with the [EMAIL_ADDRESS] increasing to 0.981 and detection accuracies for the Cargo, Tanker, and Unknown categories reaching 0.991, 0.988, and 0.963, respectively. In conclusion, the proposed improvements significantly enhanced the detection performance for low-resolution small objects and overlapping targets in complex environments. The synergy between the modules resulted in exceptional detection performance, particularly for the Unknown category, which typically features smaller targets. To further validate the superiority of the proposed algorithm, a comparative experiment was conducted between this algorithm and several mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8, and YOLOv11, using the Wuhu waterway dataset under identical experimental parameters. The results are presented in Table 4: \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**SPD-Conv** & **Detect\_SE** & **Focal-EloU** & **RReLU** & **Cargo** & **Tanker** & **Unknown** & **All** \\ \hline  & & & & 0.988 & 0.987 & 0.949 & 0.975 \\ \(\surd\) & & & & 0.985 & 0.988 & 0.956 & 0.976 \\  & \(\surd\) & & & 0.989 & 0.986 & 0.954 & 0.976 \\ \(\surd\) & \(\surd\) & & & 0.988 & 0.983 & 0.959 & 0.977 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.989 & 0.982 & 0.963 & 0.978 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.991 & 0.988 & 0.963 & 0.981 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison and display of [EMAIL_ADDRESS] effects in the ablation experiment. The experimental structure comparison is shown in Figure 6. Based on the analysis of the comparative experimental results, the proposed method demonstrates exceptional performance across three key metrics: precision, recall, and [EMAIL_ADDRESS].