HMS-Net: A Hierarchical Multilabel Fine-Grained Ship Detection Network in Remote Sensing Images - Yunchao Yang; Zhengning Zhang; Pengming Feng; Yiming Yan; Guangjun He; Shaobo Liu; Pengyong Zhang; Haorao Gao (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
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 interclass relationships and hierarchical semantic information, which exacerbates the challenges of fine-grained ship detection. To address these limitations, this article proposes hierarchical multilabel ship detection network (HMS-Net), a novel framework that integrates hierarchical multilabel classification with fine-grained ship detection. HMS-Net incorporates a parallel multilevel semantic feature extraction and fusion network, which leverages the multiscale region feature re-extraction module to perform effective feature re-extraction at different semantic levels. In addition, a top-down feature fusion mechanism, combined with the feature guidance module, facilitates efficient knowledge transfer from upper level to lower level features. Furthermore, we design a hierarchical relationship constraint loss function to explicitly model interclass 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 HMS-Net. 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.