HMS-Net: A Hierarchical Multilabel Fine-Grained Ship Detection Network in Remote Sensing Images

The advent of deep learning has significantly advanced the efficiency and accuracy of fine-grained ship detection in remote sensing images. However, existing methods often rely on flattened label supervision during training, neglecting critical inter-class relationships and hierarchical semantic information, which exacerbates the challenges of fine-grained ship detection. To address these limitations, this paper proposes HMS-Net, a novel framework that integrates hierarchical multi-label classification with fine-grained ship detection. HMS-Net incorporates a parallel multi-level semantic feature extraction and fusion network, which leverages the Multi-scale Region Feature Reextraction (MRFR) module to perform effective feature reextraction at different semantic levels. Additionally, a top-down feature fusion mechanism, combined with the Feature Guidance (FG) module, facilitates efficient knowledge transfer from upper-level to lower-level features. Furthermore, we design a hierarchical relationship constraint loss function to explicitly model inter-class relationships among ships, optimizing feature distance measurements between different classes. Extensive ablation and comparative experiments on the HRSC2016 and ShipRSImageNet datasets demonstrate the superiority of HMSNet. Moreover, our method has been successfully applied to various two-stage oriented object detection frameworks, consistently achieving performance improvements, thereby highlighting its plug-and-play versatility.