@inproceedings{
jian2023stable,
title={Stable Diffusion For Aerial Object Detection},
author={Yanan Jian and Fuxun Yu and Simranjit Singh and Dimitrios Stamoulis},
booktitle={NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI},
year={2023},
url={https://openreview.net/forum?id=eDTdaE1yTG}
}
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.
Stable Diffusion For Aerial Object Detection
Yanan Jian Fuxun Yu Simranjit Singh Dimitrios Stamoulis