What Should We Generate for UAV Detection?

Generative augmentation expands the environmental coverage for small unmanned aerial vehicle (UAV) detection, but semantically distinct conditions can lead to similar changes in the detector's features. We combine a two-stage decoupled generation method with detector-guided condition selection. To provide a shared target reference for comparing conditions, we first edit the background and then blend the source UAV back into the image using a soft mask. A frozen detector, trained on clean images, measures paired context responses to estimate condition similarities, and a backward greedy algorithm balances response coverage against pairwise overlap, selecting conditions for a user-specified weight without prescribing the subset size. The retained generated images are combined with clean images for detector training. With five candidate conditions, all selected two- to four-condition configurations achieve higher external mean AP than Clean-only and Full generation with both RT-DETRv2-S and YOLO11m. For RT-DETRv2-S, a selected three-condition subset improves external mean AP by 2.95 points over Clean-only, 0.80 over Full generation, and 2.11 over Full generation with a matched clean/generated sampling ratio. For YOLO11m, the selected subsets improve external mean AP over Full generation by 1.05-1.14 points.