## 5 Conclusions and Future Perspectives  
In this paper, a perceptual data fusion framework based on ship ST-TF was proposed for ship AR navigation assistance systems to ensure navigation safety and enhance maritime surveillance efficiency. The main contributions of this paper are as follows. First, the ship tracking system was optimized by introducing the Bi-YOLO network, which incorporates the C2f_BiFormer module to enhance the detection performance of small targets while reducing model parameters. Additionally, the more robust OC-SORT algorithm was integrated to achieve real-time and stable tracking of moving ship targets. Second, a visual localization model of maritime targets based on binocular imaging was constructed, and the absolute position estimation of maritime targets in the absence of a reference was achieved by fusing the attitude information of the camera. Subsequently, a perceptual data fusion method based on ship ST-TF was proposed. This method temporally and spatially fused the visual trajectory with the GPS trajectory from the E-AIS database. It also further integrated other dynamic and static information. Finally, a ship AR navigation assistance system was developed in conjunction with the proposed multi-source fusion perception framework. Comprehensive experiments on multi-scene MASS tracking, visual position estimation, and multi-source data fusion were conducted on the newly developed FLShip dataset to validate the robustness and effectiveness of the proposed perceptual data fusion framework based on ship ST-TF. The results of comparative experiments with various advanced object detection and tracking algorithms demonstrate that even in low-resolution, noisy, and low-brightness scenes, the framework achieves a [EMAIL_ADDRESS] of 98.7% in multi-ship detection while maintaining a MOTA between 90% and 98%, all while reducing the model's parameter count. The framework successfully extracted reliable visual trajectories of target ships and validated the accuracy of visual position estimation across multiple scenes, ensuring the accurate association between visual detection and E-AISs. The ship AR navigation assistance system based on the multi-source fusion perception framework proposed in this paper is able to enhance the ability of MASS traffic situational awareness, provide more comprehensive and intuitive navigation information for the crew, make it easier to cope with the challenges of complex waterways and ensure the safety of ship navigation. In addition, in the case of equipment failure or illegal operation of the target ship, the navigation assistance system is still able to provide critical dynamic data support for tasks such as emergency response, search and rescue, and maritime surveillance by presenting the visual latitude and longitude coordinates of the target, thus enhancing the safety level of the entire maritime field. Although the proposed framework performs well in scenarios with noise, wave interference, and low visibility, vision-based perception strategies remain constrained by imaging conditions. Additionally, with the integration of multi-sensor perception systems, optimizing real-time performance and ensuring compatibility with existing ship navigation systems are critical prerequisites for practical deployment. To address these challenges, future research will focus on the following directions to enhance system robustness and adaptability, thereby ensuring effectiveness and reliability in real-world applications:  
* Multi-sensor fusion technology: In-depth research on the fusion mechanisms of multi-source sensors, such as cameras, radars, and AISs, will be conducted. Dynamic sensor weight adjustment based on the navigation environment will provide more accurate ship motion information, further strengthening situational awareness in the era of MASS autonomous navigation and offering advanced and reliable solutions for maritime navigation.