Elaborate Information Refinement Network for Fine-Grained Object Detection in Remote Sensing Images

0pt Fine-grained object detection aims to localize and classify subcategories of objects by extracting more discriminative semantic features, which is particularly challenging for remote sensing images due to their complex backgrounds and arbitrarily oriented objects. Accurate localization with bounding box regression typically relies on detailed texture and edge information to delineate object boundaries, while fine-grained classification requires more elaborate semantic information. However, existing methods often share the same input features across the model, resulting in a mismatch between the requirements of the localization task and those of the fine-grained classification task. To address this problem, we propose a Elaborate Information Refinement Network (EIRNet), which not only effectively separates features for localization and fine-grained classification but also refines these features according to the specific requirements of each task. For fine-grained classification, we propose a Fine-grained Context Fusion Module (FCFM) to enhance the ability to extract discriminative features by expanding the receptive field. For localization, we introduce an Edge Information Sensing Module (EISM) to extract scale-invariant features by combining high-dimensional information with detailed edge information, thereby improving the network’s ability to accurately locate objects. Additionally, to extract richer fine-grained semantic information, we present a Feature Injection Module (FIM), which collects and fuses information across different levels using a unified structure. This module then distributes the refined features to appropriate levels, effectively reducing inherent information loss and enhancing the local information fusion capability of the network. Through extensive experiments, the proposed method demonstrates a mean average precision (mAP) of 80.23% on ShipRSImageNet and 50.52% on FAIR1M-v1.0, marking improvements of 4.10% and 2.54% over the current state-of-the-art approaches, respectively.