# Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles

Source: [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](<https://www.sota2.com/research/paper/68fa6b791e209131cea2e622>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:12:56.063Z

Authors: Benjamin Kiefer, David Ott, Andreas Zell

Published: 2021-12-22T22:41:02.000Z

## Abstract

Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detection from UAVs by analyzing multiple training strategies across several models. Furthermore, we analyze several different data generation and sampling parameters to provide actionable engineering advice for further scientific research. The DeepGTAV framework is available at https://git.io/Jyf5j.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.12252>)

[PDF](<https://arxiv.org/pdf/2112.12252.pdf>)

[Implementation](<https://git.io/Jyf5j>)