# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 7 Conclusions  
This paper proposes a waterway traffic situation awareness method based on heterogeneous data fusion, addressing the current insufficiency of inland waterway traffic information services and ship navigation safety issues. Firstly, to solve the problem of detecting small target ships, we improved the one-stage object detection algorithm by introducing the SPD-Conv module to enhance the feature learning capability for low-resolution small targets. Additionally, the Detect_SE module was introduced to optimize the feature extraction and channel feature integration capability. Furthermore, the Focal-EIoU loss function was adopted to improve detection accuracy in complex environments with overlapping targets. The experimental results show that our method achieves a [EMAIL_ADDRESS] of 98.1%, significantly outperforming traditional algorithms, and demonstrates good robustness in complex backgrounds and low-resolution scenarios. Our method also improves ship detection identity consistency performance, with the multi-object consistent accuracy (MOCA) reaching 92.61% in test video data, an improvement of 2.21% compared to existing algorithms. Additionally, in scenarios with target overlap and occlusion, the IDF1 score reaches 92.05%, effectively reducing target loss and mismatching issues. To address the temporal and spatial differences between AIS data and video data, we propose a matching method combining trajectory prediction and region set partitioning, which accurately aligns ship pixel coordinates with the geographic locations of AIS data. By employing trajectory prediction, we achieved time synchronization between AIS data and video detection data. The region partition matching significantly improved the accuracy and consistency of multi-source data fusion. The experimental results demonstrate that the fusion accuracy of our method can achieve an average of 94.32% across different test scenarios.  
By combining dynamically updated waterway status data from the intelligent navigation marker system with augmented reality technology, the real-time visualization of waterway traffic has been achieved. This visualization includes key information such as vessel type, speed, heading, and traffic flow, enhancing the ability of managers to perceive a waterway situation and improving decision-making efficiency. The innovative application of multi-source data fusion within the intelligent navigation marker system provides accurate local traffic perception data for waterway managers, thereby enhancing service quality for inland waterway vessels. Additionally, it establishes a foundation for future innovations in ship-to-shore interaction models within the context of inland smart shipping.  
Conceptualization, R.Z.; methodology, R.Z., Y.A., J.H. ([PERSON]) and J.H. ([PERSON]); software, R.Z. and Y.A.: validation, R.Z., Y.A. and J.H. ([PERSON]); resources, R.Z. and M.P.; data curation, Y.A. and R.Z.; writing--original draft preparation, Y.A. and R.Z.; writing--review and editing, R.Z., M.P. and S.L.; funding acquisition, M.P. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the International Association of Maritime Universities (IAMU) and the Nippon Foundation in Japan (Grant No.20240201). This work is also supported through the Guangxi Key Research and Development Plan (Grant No. GUIKE AB22080107).  
Not applicable.  
Due to confidentiality agreements with the data provider, this dataset is partially published on GitHub.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AIS & Automatic Identification System \\ CNNs & Convolutional Neural Networks \\ VTS & Vessel Traffic Services \\ RCNNs & Region-Based Convolutional Neural Networks \\ Fast-RCNN & Fast Region-Based Convolutional Network \\ Faster-RCNN & Faster Region-Based Convolutional Network \\ SSD & Single-Shot MultiBox Detector \\ VHF & Very High Frequency \\ YOLO & You Only Look Once \\ \end{tabular}