Papers › Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles
Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles
Benjamin Kiefer, David Ott, Andreas Zell
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.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | SeaDronesSee | Synth Pretrained Faster R-CNN ResNeXt-101-FPN | mAP@0.5 | 59.20 | #1 of 10 | Archive leaderboard | report |
| Object Detection | SeaDronesSee | Synth Pretrained Yolo5 | mAP@0.5 | 59.08 | #2 of 10 | Archive leaderboard | report |
| Object Detection | SeaDronesSee | Synth Pretrained EffDetD0 | mAP@0.5 | 38.74 | #5 of 10 | Archive leaderboard | report |
| Object Detection | SeaDronesSee | Yolo 5 | mAP@0.50 | 54.74 | #10 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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