Data Augmentation for Object Detection: A Review

Abstract

Deep learning has been a game changer in the field of object detection in the last decade. But all the deep learning models for computer vision depend upon large amount of data for consistent results. For real life problems especially for medical imaging, availability of enough amounts of data is not always possible. Data augmentation is a collection of techniques that can be used to extend the dataset size and improve the quality of images in the dataset by a required amount. Logically it is used to make the deep learning model independent of the counterfeit features of the data space. In this paper a comprehensive review of data augmentation techniques for object detection is done. Problem of class imbalance is also outlined with possible solutions. In addition to train time augmentation techniques an overview of test time augmentations is also presented.