Region-based Cycle-Consistent Data Augmentation for Object Detection

Florian Kluger, Christoph Reinders, Kevin Raetz, Philipp Schelske, Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn

Abstract: Roads constitute a major part of the lives of everybody. Heavy use, for instance by cars and especially trucks, and even soil movement lead to visible damages. While major roads are regularly inspected, smaller roads often lack attention. It is therefore of great interest to have camera-based systems which can automatically detect and even classify damages.This report presents a system developed by the authors as part of the Road Damage Detection and Classification Challenge at the 2018 IEEE Big Data Cup [1]. Further contributions made here are techniques to augment the small set of training data. As a major contribution we also propose refinements to the dataset and evaluation metric to improve the challenge.