Existing methods for object detection in UAV images ignored an important
challenge - imbalanced class distribution in UAV images - which leads to poor
performance on tail classes. We systematically investigate existing solutions
to long-tail problems and unveil that re-balancing methods that are effective
on natural image datasets cannot be trivially applied to UAV datasets. To this
end, we rethink long-tailed object detection in UAV images and propose the Dual
Sampler and Head detection Network (DSHNet), which is the first work that aims
to resolve long-tail distribution in UAV images. The key components in DSHNet
include Class-Biased Samplers (CBS) and Bilateral Box Heads (BBH), which are
developed to cope with tail classes and head classes in a dual-path manner.
Without bells and whistles, DSHNet significantly boosts the performance of tail
classes on different detection frameworks. Moreover, DSHNet significantly
outperforms base detectors and generic approaches for long-tail problems on
VisDrone and UAVDT datasets. It achieves new state-of-the-art performance when
combining with image cropping methods. Code is available at
this https URL

我们提出了双采样器和头部检测网络（DSHNet），它是第一个致力于解决 UAV 图像中长尾分布问题的工作。DSHNet 的关键组件包括类偏采样器（CBS）和双边框头（BBH），它们以双路径方式应对尾部类和头部类，并在不同的检测框架下显著提高了尾部类的性能。此外，DSHNet 在 VisDrone 和 UAVDT 数据集上明显优于基准检测器和通用长尾问题方法，在结合图像裁剪方法时，DSHNet 的性能达到了新的最优水平。