  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
https://github.com/we1pingyu/DSHNet
