ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images
This work addresses performance and robustness challenges for object detection in remote sensing applications, representing an incremental improvement over existing augmentation methods.
The authors tackled the problem of low object density and limited annotations in aerial image object detection by introducing ColMix, a data augmentation framework combining collage pasting and PixMix, which improved precision and recall and enhanced robustness to image corruptions.
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.