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.

针对航空图像的合成数据增强框架，包括通过稀疏到稠密的感兴趣区域提取来弥合语义差距，使用低秩适应（LORA）对扩展训练进行优化，最后使用复制粘贴方法将合成物体与背景组合，为航空对象检测提供了一种细致入微的合成数据方法。

稳定的航空目标检测扩散

Stable Diffusion For Aerial Object Detection

In contrast to the generic object, aerial targets are often non-axis aligned
with arbitrary orientations having the cluttered surroundings. Unlike the
mainstreamed approaches regressing the bounding box orientations, this paper
proposes an effective adaptive points learning approach to aerial object
detection by taking advantage of the adaptive points representation, which is
able to capture the geometric information of the arbitrary-oriented instances.
To this end, three oriented conversion functions are presented to facilitate
the classification and localization with accurate orientation. Moreover, we
propose an effective quality assessment and sample assignment scheme for
adaptive points learning toward choosing the representative oriented reppoints
samples during training, which is able to capture the non-axis aligned features
from adjacent objects or background noises. A spatial constraint is introduced
to penalize the outlier points for roust adaptive learning. Experimental
results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD
and DIOR-R, demonstrate the efficacy of our proposed approach. The source code
is availabel at: this https URL

本文提出了一种基于自适应点学习的方法来检测非轴对齐的空中目标，该方法采用自适应点表示法来捕捉任意方向实例的几何信息，并结合三种有向转换函数以及有效的质量评估和样本分配方案，使用空间约束惩罚离群点进行自适应学习，实验结果表明我们的方法在四个具有挑战性的空中数据集上表现良好。