Global Edge Information Guidance With Polarity-Aware Attention for Satellite Object Detection

Object detection in remote sensing imagery remains challenging due to extreme scale variations, and deployment computational inefficiency. Existing transformer-based detectors exhibit incomplete feature representations stemming from limited scale-awareness in handling multi-scale objects, insufficient modeling of negative-polarity interactions and uniform attention distributions. To address these limitations, this paper proposes a novel framework integrating a Global Edge Information Extraction (GEIE) module and Polarity-aware Linear Attention-based Internal Feature Interaction (PLAIFI) module. The GEIE module consists of two components to integrate multi-scale edge features: a Poly-Resolution Edge Feature Producer (PREFP) module and an Edge Fusion module. The PREFP module extracts edge information using a 3D SobelConv branch applied to shallow features and generates a multi-scale feature map to augment the hybrid encoder’s multi-scale representations with edge-aware priors. The Edge Fusion module supports flexible integration to enrich boundary-aware representations. To establish complementary feature enhancement, the PLAIFI module decomposes query-key pairs into positive/negative polarity components via learnable projection matrices, explicitly models same- and opposite-signed interactions through dual-polarity branches with polarity-sensitive coefficient matrices, and employs channel-wise learnable power functions to sharpen attention distributions by reducing entropy - enhancing scale-awareness while maintaining linear complexity. Extensive experiments demonstrate state-of-the-art performance: 95.0% mAP (+4.6%) on HRSC2016 (small-scale ships), 73.0% mAP (+5.9%) on DOTAv1.5 (multi-class objects), and 93.9% mAP on SIRSTv2 (tiny-scale infrared targets), achieved with 11.4% GFLOPs/11% Params increase over baseline RT-DETR. The principal innovation constitutes a synergistic framework where edge-aware feature recalibration and polarity-driven attention refinement mutually reinforce each other, establishing new state-of-the-art performance while maintaining deployment-friendly computational efficiency on satellite imagery. The source code will be available at https://github.com/Keykeykeykeykeykey/Global_Edge_Guidance.