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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.08956 |
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| _version_ | 1866918053360435200 |
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| author | Yoon, DaeEun Kim, Semin Yoo, SangWook Lee, Jongha |
| author_facet | Yoon, DaeEun Kim, Semin Yoo, SangWook Lee, Jongha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Augmentation For Small Object using Fast AutoAugment Yoon, DaeEun Kim, Semin Yoo, SangWook Lee, Jongha Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Data Augmentation For Small Object using Fast AutoAugment |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.08956 |