The ability to classify and recognize maritime targets based on visual images plays an important role in advancing ship intelligence and digitalization. The current target recognition algorithms for common maritime targets, such as buoys, reefs, other ships, and bridges of different colors, face challenges such as incomplete classification, low recognition accuracy, and a large number of model parameters. To address these issues, this paper proposes a novel maritime target recognition method called DTI-YOLO (DualConv Triple Attention InnerEIOU-You Only Look Once). This method is based on a triple attention mechanism designed to enhance the model’s ability to classify and recognize buoys of different colors in the channel while also making the feature extraction network more lightweight. First, the lightweight double convolution kernel feature extraction layer is constructed using group convolution technology to replace the Conv structure of YOLOv9 (You Only Look Once Version 9), effectively reducing the number of parameters in the original model. Second, an improved three-branch structure is designed to capture cross-dimensional interactions of input image features. This structure forms a triple attention mechanism that accounts for the mutual dependencies between input channels and spatial positions, allowing for the calculation of attention weights for targets such as bridges, buoys, and other ships. Finally, InnerEIoU is used to replace CIoU to improve the loss function, thereby optimizing loss regression for targets with large scale differences. To verify the effectiveness of these algorithmic improvements, the DTI-YOLO algorithm was tested on a self-made dataset of 2300 ship navigation images. The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors.

UAV-based ship segmentation is important for berthing and unberthing monitoring, ship–berth distance estimation, and situational awareness in port waters. However, direct video mask propagation with Segment Anything Model 2 (SAM 2) remains susceptible to local contour degradation in high-resolution UAV videos containing weak berth-side boundaries, adjacent tugboats, quay-side structures, water-surface reflections, and target-scale variations. To address this problem, a constrained local boundary refinement method is proposed for target-ship segmentation. The method follows a training-free, first-frame-mask-initialized semi-supervised video object segmentation setting, with all SAM 2 parameters remaining frozen. ROI Boundary Re-Inference first enhances weak contours within local target neighborhoods. Prompt-Consensus Refinement then retains boundary candidates consistently supported by multiple structured prompt variants. Finally, Boundary-Constrained Non-Erosive Fusion restricts supplementation to a narrow neighborhood of the propagated boundary and incorporates reliable candidates without deleting the original foreground. Experiments on a self-built UAV berthing and unberthing video dataset show that the proposed method improves Boundary F@5 px from 89.68% to 94.03% and J&F from 94.08% to 96.39%. These results demonstrate that the proposed method improves target-ship boundary delineation without model training or fine-tuning.