AR-DETR: A Lightweight RT-DETR-Based Framework for Robust Aircraft Detection in Complex Remote Sensing Imagery

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Abstract
Detecting aircraft targets in optical remote sensing(RS) imagery is essential for airspace management, airport operations, and defense applications. However, RS aircraft detection remains challenging due to the prevalence of small targets, dense spatial arrangements, multi-scale target variations, and complex backgrounds. To address these issues, a lightweight detection model, AR-DETR, is developed for aircraft detection in RS imagery, building upon the Real-Time Detection Transformer (RT-DETR) framework. Firstly, a cross-aware feature enhancement module is proposed to strengthen both spatial and channel feature interactions, effectively enhancing the representation of small and weakly textured aircraft under cluttered backgrounds. Next, a dual-scale frequency fusion module is designed, which combines multi-scale depth-wise convolutions in the spatial domain with discriminative frequency selection in the frequency domain, thereby improving structural pattern extraction across different scales while suppressing background noise. Finally, an edge-Gaussian contextual enhancement module is developed to replace the conventional neck block, enabling fine-grained detail preservation and contextual enhancement through edge-guided Gaussian modulation with reduced computational cost. Results on the DOTA1.0 dataset demonstrate that AR-DETR achieves superior accuracy and efficiency compared to state-of-the-art detectors. Further validation on the SODA-A dataset, which emphasizes small-and medium-sized aircraft, confirms the proposed model's robustness and generalization under complex RS conditions.
Funding Information
  • National Natural Science Foundation of China (31601227)

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