  Aerial object detection is a challenging task, in which one major obstacle
lies in the limitations of large-scale data collection and the long-tail
distribution of certain classes. Synthetic data offers a promising solution,
especially with recent advances in diffusion-based methods like stable
diffusion (SD). However, the direct application of diffusion methods to aerial
domains poses unique challenges: stable diffusion's optimization for rich
ground-level semantics doesn't align with the sparse nature of aerial objects,
and the extraction of post-synthesis object coordinates remains problematic. To
address these challenges, we introduce a synthetic data augmentation framework
tailored for aerial images. It encompasses sparse-to-dense region of interest
(ROI) extraction to bridge the semantic gap, fine-tuning the diffusion model
with low-rank adaptation (LORA) to circumvent exhaustive retraining, and
finally, a Copy-Paste method to compose synthesized objects with backgrounds,
providing a nuanced approach to aerial object detection through synthetic data.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning