Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection

TL;DR

This paper introduces a generative approach that creates artificial aerial images with vehicles to enhance deep learning detection models, significantly improving vehicle detection accuracy when real data is scarce.

Contribution

It proposes a novel method of generating artificial aerial images with vehicles to address data scarcity in vehicle detection tasks.

Findings

Adding artificial images improves detection accuracy by up to 0.70 AP points.

Artificial images help close the performance gap when real data is limited.

Background composition significantly influences detection performance.

Abstract

Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A major challenge in such approaches is the limited amount of data that arises, for example, when more specialized and rarer vehicles such as agricultural machinery or construction vehicles are to be detected. This lack of data contrasts with the enormous data hunger of deep learning methods in general and object recognition in particular. In this article, we address this issue in the context of the detection of road vehicles in aerial images. To overcome the lack of annotated data, we propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance. In cases of very limited or even no real-world images, we observe an improvement in average precision of up to 0.70 points. We address the remaining performance gap to real-world datasets by analyzing the effect of the image composition of background and objects and give insights into the importance of background.