P2R-OBB: A Unified Framework for Multi-Scale and Orientation-Aware Ship Detection

Unmanned aerial vehicles (UAVs) and satellites play a crucial role in maritime surveillance, yet ship detection in remote sensing imagery remains challenging due to small object sizes, arbitrary orientations, and cluttered backgrounds. Existing detectors struggle to simultaneously preserve fine-grained details for small ships and suppress background noise. To tackle this, we propose P2R-OBB, a YOLOv8-OBB–based framework that introduces an additional P2 feature pyramid level together with a dynamically recalibrated attention mechanism. The P2 feature pyramid retains high-resolution shallow features that are critical for small-object detection, yielding a substantial 17.5% mAP50 improvement on the complex DOTA v1-ship dataset. In parallel, the dynamic attention module adaptively recalibrates feature responses to emphasize ships while suppressing irrelevant background structures, delivering a 4.7% mAP50 gain on the SSDD+ dataset. When combined, these components exhibit a strong synergistic effect, achieving a substantial 11.1% absolute mAP50 improvement on the complex DOTA v1-ship dataset and setting a new state of the art for oriented ship detection. Our framework offers a robust and efficient solution, with its key contributions particularly demonstrated in detecting small and arbitrarily oriented ship targets in remote sensing imagery.