Robust Fine-Grained Oriented Ship Detection for Remote Sensing imagery via Controllable Generative Pretraining

Fine-grained ship recognition in remote sensing imagery is essential for maritime applications. However, its development is hindered by two challenges: (1) the limited granularity of existing ship detection datasets, and (2) the disturbance of complex maritime conditions as well as the arbitrary ship orientations and distributions. To address the first issue, we annotated a large-scale fine-grained ship instance detection dataset (LAFI), comprising 48,717 ship instances worldwide with 49 categories. To tackle the challenges of marine disturbance and diverse ship status, we proposed a controllable generative knowledge-driven ship detection framework (COSD). It employs a controllable generative model guided by ship-marine knowledge to generate millions of synthetic images that not only preserve ship structures but also cover diverse sea and weather conditions for robust pretraining. Furthermore, a heterogeneous feature alignment decoder is designed to align multi-modal metrics of orientation and distribution features in the latent space, allowing for accurate representation of diverse ship status. Extensive experiments on two benchmark datasets showed that our method respectively increased 0.093 and 0.129 mean average precision (mAP) over SOTA methods, particularly in scenarios involving small, densely packed and arbitrary oriented ships.