Rongjiong Wu

Automatic Identification System (AIS)-based vessel trajectory prediction is essential for maritime traffic management and navigation safety. Existing deep learning methods typically model vessel motion within a unified temporal representation space, which may entangle long-term navigation trends with local maneuvering behaviors. However, vessel trajectories inherently exhibit heterogeneous dynamics, including steady route evolution and non-stationary maneuver perturbations. To address this issue, this paper proposes MD-EDTCNFormer, a motion-decoupled dual-stream framework for vessel trajectory prediction. A Global Navigation Dynamics Encoder is designed to capture dominant route-level temporal evolution from raw AIS sequences, while a Residual Maneuver Dynamics Encoder explicitly models maneuver-related local perturbations through state transition residual representations. In addition, a state-adaptive motion aggregation mechanism is introduced to dynamically balance global navigation dependencies and local maneuver-aware dynamics under different navigation states. Depthwise separable temporal convolution and efficient channel attention are further integrated to suppress redundant temporal-channel coupling and emphasize dynamically dominant motion cues. Experiments on a real-world AIS dataset from the Zhoushan coastal area demonstrate the effectiveness of the proposed framework under coastal traffic conditions, and show improvements in prediction accuracy and trajectory stability compared with representative baseline methods.

In response to challenges such as narrow visibility for ship navigators, limited field of view from a single camera, and complex maritime environments, this study proposes panoramic visual perception-assisted navigation technology. The approach includes introducing a region-of-interest search method based on SSIM and an elliptical weighted fusion method, culminating in the development of the ship panoramic visual stitching algorithm SSIM-EW. Additionally, the YOLOv8s model is improved by increasing the size of the detection head, introducing GhostNet, and replacing the regression loss function with the WIoU loss function, and a perception model yolov8-SGW for sea target detection is proposed. The experimental results demonstrate that the SSIM-EW algorithm achieves the highest PSNR indicator of 25.736, which can effectively reduce the stitching traces and significantly improve the stitching quality of panoramic images. Compared to the baseline model, the YOLOv8-SGW model shows improvements in the P, R, and mAP50 of 1.5%, 4.3%, and 2.3%, respectively, its mAP50 is significantly higher than that of other target detection models, and the detection ability of small targets at sea has been significantly improved. Implementing these algorithms in tugboat operations at ports enhances the fields of view of navigators, allowing for the identification of targets missed by AISs and radar systems, thus ensuring operational safety and advancing the level of vessel intelligence.

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.