AeroGRT: Geometry-Aware Global–Local Routing Transformer with Query-Reliability Graphs for UAV Object Recognition in Complex Scenes

UAV visual perception is increasingly important for intelligent transportation monitoring, infrastructure inspection, emergency response, and low-altitude surveillance. However, reliable target localization remains challenging in complex scenes where targets are small, densely distributed, weakly textured, partially occluded, or visually similar to the background. Motivated by attention-based feature fusion and the routing attention design of AnisoNet, this paper proposes AeroGRT, a framework integrating global–local feature encoding and query-reliability graph attention for UAV visual perception in complex scenarios. The proposed framework combines a CNN-based local encoder, a Transformer-based global encoder, an adaptive global optimization module, a foreground–background fusion attention module, a cross-axis boundary attention mechanism, a query-reliability graph attention module, and a scale–altitude routing detection head. The architecture is designed to integrate local spatial detail with long-range semantic context, preserve target boundary information, and reduce unreliable query–key associations between foreground targets and background clutter. AeroGRT was evaluated on GOT-10k and TrackingNet for object tracking and on VisDrone and TinyPerson for small-object detection, using task-specific protocols and metrics. These evaluations assess performance under occlusion, deformation, low contrast, background clutter, and long-range observation. This study investigates the joint modeling of local detail, global context, boundary cues, and query reliability for accurate UAV target localization in challenging environments.