  Object detection in aerial images is a challenging task due to the following
reasons: (1) objects are small and dense relative to images; (2) the object
scale varies in a wide range; (3) the number of object in different classes is
imbalanced. Many current methods adopt cropping idea: splitting high resolution
images into serials subregions (chips) and detecting on them. However, some
problems such as scale variation, object sparsity, and class imbalance exist in
the process of training network with chips. In this work, three augmentation
methods are introduced to relieve these problems. Specifically, we propose a
scale adaptive module, which dynamically adjusts chip size to balance object
scale, narrowing scale variation in training. In addtion, we introduce mosaic
to augment datasets, relieving object sparity problem. To balance catgory, we
present mask resampling to paste object in chips with panoramic segmentation.
Our model achieves state-of-the-art perfomance on two popular aerial image
datasets of VisDrone and UAVDT. Remarkably, three methods can be independently
applied to detectiors, increasing performance steady without the sacrifice of
inference efficiency.
