Jiajun Chen

Small-object details in high-resolution unmanned aerial vehicle (UAV) imagery are weakened when wide-area scenes are resized for detector input. Increasing input resolution or exhaustively processing local regions can recover spatial detail, but allocate computation without distinguishing regional value after global detection. This paper proposes MRU-YOLO, a selective local re-observation framework guided by marginal re-observation utility (MRU), which measures the expected detection benefit of reprocessing a candidate region after one global forward pass. MRU-YOLO constructs prediction-conditioned states for nine candidate regions from global detections and region geometry. A learned utility regressor ranks the candidates and selects the two highest-ranked regions for local inference. Source-aware fusion integrates complementary global and local predictions while resolving cross-source conflicts. The pipeline requires no modification to the detector backbone, neck, or detection head. Across three independent runs, MRU-YOLO reached mean mAP50–95 values of 41.82% on SeaDronesSee ODv2 and 21.72% on VisDrone2019-DET, improving YOLO11n-640 by 2.32 and 3.28 percentage points, respectively. Class-wise AP50–95 improved in four of five maritime categories and nine of ten urban categories, while the remaining urban category was effectively unchanged. Learned selection also achieved higher utility capture and normalized discounted cumulative gain at rank 2 (NDCG@2) than predicted density on both datasets. Under batch-1 FP16 inference on an NVIDIA GeForce RTX 3090, the end-to-end pipeline achieved 30.86 FPS on SeaDronesSee ODv2 and 33.24 FPS on VisDrone2019-DET. MRU-YOLO concentrates local inference on regions with the highest expected detection contribution under a fixed local-processing budget.