ColMix -- A Simple Data Augmentation Framework to Improve Object
  Detector Performance and Robustness in Aerial Images
Cuong Ly Grayson Jorgenson Dan Rosa de Jesus Henry Kvinge Adam Attarian Yijing Watkins
In the last decade, Convolutional Neural Network (CNN) and transformer based
object detectors have achieved high performance on a large variety of datasets.
Though the majority of detection literature has developed this capability on
datasets such as MS COCO, these detectors have still proven effective for
remote sensing applications. Challenges in this particular domain, such as
small numbers of annotated objects and low object density, hinder overall
performance. In this work, we present a novel augmentation method, called
collage pasting, for increasing the object density without a need for
segmentation masks, thereby improving the detector performance. We demonstrate
that collage pasting improves precision and recall beyond related methods, such
as mosaic augmentation, and enables greater control of object density. However,
we find that collage pasting is vulnerable to certain out-of-distribution
shifts, such as image corruptions. To address this, we introduce two simple
approaches for combining collage pasting with PixMix augmentation method, and
refer to our combined techniques as ColMix. Through extensive experiments, we
show that employing ColMix results in detectors with superior performance on
aerial imagery datasets and robust to various corruptions.