# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 6 Discussion  
### Challenges in Multi-Source Data Fusion  
This paper addresses various technical challenges in vessel detection, tracking, and multi-source data fusion within inland waterway navigation, proposing a deep learning-based method for waterway traffic situation awareness. To overcome issues such as difficulty in detecting small targets, severe occlusion, and AIS data latency in inland waterway environments, the paper enhances a one-stage object detection algorithm. The SPD-Conv module is employed to improve the detection of small target details, the squeeze excitation module is introduced to optimize feature extraction, and the Focal-EIoU loss function is adopted to enhance the algorithm's adaptability in detecting overlapping targets and handling complex scenes. Additionally, based on vessel trajectory tracking algorithms, the paper combines appearance and motion features to address the continuity problem of target trajectories under occlusion. In terms of multi-source data fusion, the paper proposes a matching method that combines trajectory prediction and region set partitioning to address the temporal and spatial differences between AIS data and video data, achieving accurate fusion of video-detected vessels with AIS vessels in both time and space. These improvements significantly enhance detection accuracy, tracking stability, and data fusion effectiveness in complex inland waterway environments.  
Compared to existing inland waterway services, this paper achieves a more comprehensive improvement in waterway traffic situation awareness. Traditional waterway service systems are typically based on a single data source (such as AIS or video data), which presents significant shortcomings in terms of real-time performance, accuracy, and environmental adaptability. This paper combines AIS data with video detection data to overcome the limitations of a single data source in target identification and dynamic information acquisition, significantly enhancing data fusion capabilities in complex environments. Compared to AIS-based data analysis alone, this paper can more accurately restore the vessel's trajectory information and perceive its visual appearance; compared to video-based monitoring methods alone, this paper utilizes AIS data to improve the stability of target identity confirmation and trajectory tracking. Meanwhile, the application of the intelligent beacon system enhances data real-time performance and further improves the intelligence level of the system.  
The primary contribution of this paper is the development of a refined inland waterway traffic situation awareness system. This system is based on an augmented reality (AR) framework that integrates multi-source data fusion with real-time information visualization, enabling a comprehensive representation of dynamic traffic conditions in inland waterways. The proposed framework dynamically displays key parameters such as vessel type, heading, speed, and traffic flow while providing real-time vessel density statistics and traffic flow predictions. These capabilities assist shipping managers in effectively monitoring waterway traffic conditions and formulating optimized navigation strategies. By deeply integrating multi-source data and combining visual detection with dynamic behavior prediction, the proposed approach enhances situational awareness and overcomes the limitations of traditional waterway service systems, which rely on single data sources and static perception. Furthermore, while the proposed method is specifically designed for the Yangtze River, it is theoretically adaptable to other waterway environments. In more complex maritime settings, it can be flexibly tailored to different operational needs, offering essential technical support for the advancement of efficient and intelligent inland waterway traffic management.