用快速自动增强优化小目标检测，提升20%性能。
Data Augmentation For Small Object using Fast AutoAugment
- 基于Fast AutoAugment搜索小目标专用数据增强策略
- 在DOTA数据集上实现小目标检测性能提升20%
- 适合需要提升小目标检测精度的研究与应用
近年来，目标检测性能取得了巨大进展。然而，尽管如此，小目标的检测性能仍显著低于大目标。小目标检测是计算机视觉中最具挑战性且重要的问题之一。为提升小目标检测性能，我们提出一种基于Fast AutoAugment的最优数据增强方法。通过该方法，可快速找到能有效缓解小目标检测性能下降的最优增强策略，在DOTA数据集上实现了20%的性能提升。
原文摘要 · Abstract (English)
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.
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