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

AMRNet: Chips Augmentation in Aerial Images Object Detection