In recent years, there has been tremendous progress in object detection performance. However, despite these advances, the detection performance for small objects is significantly inferior to that of large objects. Detecting small objects is one of the most challenging and important problems in computer vision. To improve the detection performance for small objects, we propose an optimal data augmentation method using Fast AutoAugment. Through our proposed method, we can quickly find optimal augmentation policies that can overcome degradation when detecting small objects, and we achieve a 20% performance improvement on the DOTA dataset.
@article{arxiv.2506.08956,
title = {Data Augmentation For Small Object using Fast AutoAugment},
author = {DaeEun Yoon and Semin Kim and SangWook Yoo and Jongha Lee},
journal= {arXiv preprint arXiv:2506.08956},
year = {2025}
}
Comments
Accepted and published in the USB Proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2023), Ume{\aa}, Sweden, June 19--22, 2023, ISBN 978-91-527-7293-5, pp.\ 12--21