Collaborative prior and confidence learning network for point-supervised oriented SAR ship detection

Oriented ship detection in synthetic aperture radar (SAR) images is essential for maritime surveillance, but remains challenging because obtaining reliable rotated bounding box (RBox) annotations is time-consuming. In this paper, we propose a collaborative prior and confidence learning network (PCLNet) for point-supervised oriented ship detection. Single-point annotations are cost-effective but provide limited geometric cues, leading to inaccurate localization and unstable pseudo-label quality. PCLNet addresses this limitation with a teacherstudent framework that stabilizes pseudo-label learning and improves target-domain reliability. The network further incorporates three modules: reliability-aware confidence learning (RACL), saliency-guided label refinement (SLRM), and a two-stage pseudo rotated bounding box refinement (PRR) process. The RACL predicts an intersection over union quality score for each proposal and integrates it with class confidence to align classification and localization. The SLRM aggregates multilevel features to generate a SAR-aware saliency prior that highlights ships and suppresses nontarget interference. This prior provides spatial guidance for localization and pseudo-label refinement during teacher–student training. Guided by this prior, PRR progressively refines coarse pseudo RBoxes into high-quality pseudo labels and improves point–RBox consistency. Experiments on two SAR ship datasets demonstrate competitive accuracy and strong robustness in both inshore and offshore scenes while requiring only single-point annotations.