Reasoning-DETR: Contextual and Relational Query Learning for Aerial Object Detection

Object detection has progressed rapidly from two stage and one stage frameworks to Transformer detectors. Among them, DETR formulates detection as set prediction and allows object queries to exchange contextual information through attention. However, its direct application to aerial imagery remains limited. Tiny objects are easily suppressed by complex backgrounds, while their number and spatial distribution vary considerably across scenes. A fixed query budget therefore cannot accommodate different search demands, and interactions based on feature similarity may introduce unreliable background or neighboring evidence into ambiguous queries. To address these issues, we propose Reasoning-DETR, which couples adaptive object search with query reasoning based on uncertainty. First, a dynamic query allocator guided by scene context combines regional density prediction with Scene Memory to determine the number and spatial distribution of supplementary queries. Second, the query reasoner retrieves category prototypes for ambiguous queries and selectively aggregates evidence from reliable nearby objects. A reliability gate suppresses relational messages that conflict with the current query representation. Both modules are integrated into the original RT-DETR pipeline without requiring additional scene or relation annotations. Experiments on VisDrone and AI-TOD-V2 demonstrate consistent improvements in aerial and tiny-object detection, with improved query coverage and recognition of weak objects at a modest computational overhead.