ABSTRACT: Effective multi-scale feature representation and focused attention on critical objects are essential for accurate perception of waterborne navigation scenes. To address the insufficient exploitation of multi-scale information in existing methods that leads to imprecise segmentation, this study proposes a real-time semantic segmentation method for waterborne navigation scenes through multi-scale information enhancement and importance-weighted optimization. First, DDRNet-23-slim is selected as the backbone network for feature extraction. An edge-guided branch is embedded into its shallow layers, and a Dynamic Feature Fusion Module (DFFM) is constructed by integrating a lightweight hybrid attention mechanism, effectively enhancing multi-scale feature interaction capabilities. Second, the loss function is improved using an importance-weighted strategy to prioritize critical objects during training. Finally, a parameter-free attention mechanism is introduced in the upsampling stage, maintaining real-time performance while ensuring segmentation stability for key objects under complex background interference. Evaluations on the On_Water and Seaships datasets demonstrate that the proposed method achieves mIoU scores of 83.1% and 73.2%, respectively, with ship segmentation accuracy reaching 88.2% on On_Water. The inference speed attains 69.1 FPS, outperforming mainstream real-time segmentation models (e.g., DDRNet, STDC) in balancing accuracy and efficiency. Notably, it exhibits stronger robustness in complex inland river scenarios with dense shore structures and numerous small targets.

KEYWORDS: Collision Avoidance, Computer Vision, Artificial Intelligence (AI), Navigation systems, Autonomous Ships, Semantic Segmentation, Obstacle Detection, Water Traffic Management

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