Exploiting Discriminating Features for Fine-Grained Ship Detection in Optical Remote Sensing Images

Fine-grained remote sensing ship detection is crucial in a variety of fields, such as ship safety, marine environmental protection, and maritime traffic management. Despite recent progress, current research suffers from the following three major challenges: insufficient features representation, conflicts in shared features, and inappropriate anchor labeling strategy, which significantly impede accurate fine-grained ship detection. To address these issues, we propose FineShipNet as a solution. Specifically, we first propose a novel blend synchronization module, which aims to facilitate the coutilization of semantic information from top-level and bottom-level features and minimize information redundancy. Subsequently, the blend feature maps are fed into a novel polarized feature focusing module, which decouples the features used in classification and regression to create task-specific discriminating features maps. Meanwhile, we adopt the adaptive harmony anchor labeling and propose a novel metric, harmony score, to choose high-quality anchors that can effectively capture the discriminating features of the target. Extensive experiments on four fine-grained remote sensing ship datasets (HRSC2016, DOSR, FGSD2021, and ShipRSImageNet) demonstrate that our FineShipNet outperforms current state-of-the-art object detection methods, achieving superior performance with mean average precision scores of 81.3%, 68.5%, 85.7%, and 63.9%, respectively.