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2023

Stable Diffusion For Aerial Object Detection

Jian, Yanan, Yu, Fuxun, Singh, Simranjit et al.

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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.

Stable Diffusion For Aerial Object Detection · Around

Built on

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F. Ozge Unel, Burak Oguz Özkalayci, and Cevahir Çigla · 2019
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Object detection in aerial images: A large-scale benchmark and challenges
Jian Ding, Nan Xue, Gui-Song Xia, Xiang Bai, Wen Yang, Michael Yang, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang · 2021
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