Infrared Small Ship Target Segmentation Based on Semantic Distance Alignment and Local Evidence Selection

Infrared detection of small ship targets is crucial for maritime surveillance and early warning. However, low-brightness, low-pixel-count vessels are easily confused with sea clutter and sensor noise. Existing multi-scale networks can enhance local responses and aggregate contextual features, but dense cross-scale interactions propagate these clutter responses, and mask-level supervision cannot clearly distinguish the vessel from the target-like background. To address this issue, we propose a novel infrared small-ship semantic segmentation method called TALES-Net based on semantic distance alignment and local evidence selection, which consists of a Local Evidence Selection Module (LESM) and a Semantic Distance Alignment Loss (SDAL). LESM enhances center-to-surround contrast and selectively propagates reliable evidence through sparse cross-scale channel interactions and spatially gated decoding. SDAL attracts vessel features to a learnable target prototype while rejecting target-like background features. Our proposed TALES-Net method effectively enhances the ability to distinguish weak target vessels from clutter backgrounds, thereby improving its internal representation capabilities in infrared small target detection. In experiments on the IRS-Ship dataset and controlled ablation, TALES-Net effectively improves target segmentation probability while maintaining a low false alarm rate. Extensive experiments on the IRS-Ship dataset show that TALES-Net achieves pixel accuracy (pixAcc), foreground intersection over union (IoU), target detection probability (PD), and false alarm rate (FA) of 95.84%, 91.70%, 87.70%, and 0.42%, respectively. These results demonstrate that LESM and SDAL can effectively improve the sensitivity of weak target ships and suppress false alarms under complex sea clutter conditions.