# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments
### Multi-Source Data Fusion Experiment
Compared to other mainstream object detection algorithms, such as Faster R-CNN, SSD, YOLOv8, and YOLOv11, the proposed method achieved a precision of 0.971, significantly surpassing Faster R-CNN's 0.872 and YOLOv8's 0.952 and indicating our method's superior ability to reduce false positives. In terms of recall, the proposed method also exhibits strong performance, demonstrating excellent target detection coverage. Regarding the [EMAIL_ADDRESS] metric, the proposed method outperforms all other mainstream algorithms. These results clearly highlight the effectiveness of the optimization strategy in the proposed object detection method, particularly in complex scenarios, where it achieves higher detection accuracy and robustness. This, in turn, provides more reliable technical support for inland waterway traffic situation awareness. Additionally, the Table 5 presents the MOCA, IDP, IDR, and IDF\({}_{1}\) results for various object detection algorithms across five video segments. Compared to other object detection algorithms, the proposed improved algorithm significantly reduces missed and false detections in complex scenarios, resulting in more stable vessel detection performance in crowded and challenging environments. \begin{table}
\begin{tabular}{c c c c} \hline
**Modal** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline Faster-RCNN & 0.872 & 0.807 & 0.891 \\ SSD & 0.963 & 0.927 & 0.972 \\ YOLOv8 & 0.952 & 0.943 & 0.975 \\ YOLOv11 & 0.968 & 0.958 & 0.979 \\ OURS & 0.971 & 0.967 & 0.981 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of object detection experiment results. \begin{table}
\begin{tabular}{c c c c c} \hline
**Modal** & **MOCA** & **IDP** & **IDR** & **IDF\({}_{1}\)** \\ \hline Faster-RCNN & 76.25 & 69.08 & 69.83 & 68.21 \\ SSD & 81.39 & 85.58 & 78.68 & 81.24 \\ YOLOv8 & 85.72 & 89.68 & 77.08 & 83.12 \\ YOLOv11 & 90.40 & 92.45 & 87.56 & 90.17 \\ OURS & 92.61 & 94.48 & 91.92 & 92.05 \\ \hline \end{tabular}
\end{table} 
Table 5: Evaluation of ship identity consistency. Figure 6: Comparison of experimental results: (**a**) different algorithms’ [EMAIL_ADDRESS] contrast; (**b**) different algorithms’ precision contrast; (**c**) different algorithms’ recall contrast. * Ship Target Detection in Harsh Channel Environments  
To demonstrate that our system can maintain good detection performance even under adverse weather conditions, we selected images from foggy and nighttime channel environments for ship target detection. The comparison of the processed images is shown in Figures 7 and 8:  
Figure 9 compares the performance of object detection in images before and after processing using our improved single-stage object detection algorithm:  
Figure 10 shows the comparison results, after the vessel images in adverse conditions were processed, the contrast between the vessels and the background was significantly improved. This enhancement strengthens the relevant feature information, highlighting the key characteristics of the vessel targets. As a result, the accuracy of vessel recognition is greatly improved, laying a solid foundation for the subsequent feature extraction of maritime targets and enhancing vessel identification performance in challenging environments. Figure 8: Comparison of nighttime illumination enhancement effects: (**a**) original nighttime image; (**b**) image after illumination enhancement. Figure 7: Comparison of dehazing effects: (**a**) original foggy image; (**b**) image after dehazing. * Waterway Video and AIS Data Fusion Based on Set Region Division  
In this section, we conducted vessel data fusion experiments. First, due to the latency inherent to AIS, there is a certain level of error when matching AIS data with video in real time. To minimize this error, we employed trajectory prediction to synchronize the AIS data with the video data. The results are presented in Figure 11.  
During the data fusion stage, we first divide the camera's field of view into six smaller regions.