SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection

Abstract 

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, synthetic data presents a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrated that SynDroneVision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition.  

Paper

Published in the Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025)!

SynDroneVision is presented in the paper SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection by Tamara R. Lenhard, Andreas Weinmann, Kai Franke, and Tobias Koch. This work is published in the Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025).

The preprint is currently available on ArXiv: here

The final version is now published in the proceedings of WACV 2025: here 

Dataset Details

SynDroneVision comprises a total of 140,038 annotaed RGB images (131,238 for training, 8,800 for validation, and 4,000 for testing), featuring a resolution of 2560x1489 pixels. All images are recorded in a sequential manner using Unreal Engine 5.0 in combination with Colosseum. Apart from drone images, SynDroneVision also includes ~7% of background images (i.e., imag frames without drone instances).

Annotation Format: Annotations (bounding boxes) are provided via text files according to the YOLO standard format

<object-class> <x> <y> <width> <height>

Here, <x> and <y> represent the normalized coordinates of the bounding box center, while <width> and <height> denote the normalized bounding box wisth and height. In SynDroneVision, <object-class> is always 0, indicating the drone class.

Download

The SynDroneVision dataset offers around 900 GB of data dedicated to image-based drone detection. To facilitate the download process, we have partitioned the dataset into smaller sections. Specifically, we have divided the training data into 10 segments, organized by sequences.

Annotations are available below, with image data accessible via the following links:

Dataset Split
Sequences
File Name
Link
Size (GB)

Training Set
Seq. 001 - 009
images_train_seq001-009.zip
Training images PART 1
57

Seq. 010 - 018
images_train_seq010-018.zip
Trainng images PART 2
95.4

Seq. 019 - 027
images_train_seq019-027.zip
Training images PART 3
96.2

Seq. 028 - 035
images_train_seq028-035.zip
Training images PART 4
83.9

Seq. 036 - 043
images_train_seq036-043.zip
Training images PART 5
77.1

Seq. 044 - 050
images_train_seq044-050.zip
Training images PART 6
84.7

Seq. 051 - 056
images_train_seq051-056.zip
Training images PART 7
86.8

Seq. 057 - 065
images_train_seq057-065.zip
Training images PART 8
86.2

Seq. 066 - 070
images_train_seq066-070.zip
Training images PART 9
75.7

Seq. 071 - 073
images_train_seq071-073.zip
Training images PART 10
38.5

Validation Set
Seq. 001 - 073
images_val.zip
Validation images
55.2

Test Set
Seq. 001 - 073
images_test.zip
Test images
26.5

Citation

If you find SynDroneVision helpful in your research, we kindly ask that you cite the associated paper. Below is the citation in BibTeX format for your convenience:

BibTeX:

@INPROCEEDINGS{10943801,
author={Lenhard, Tamara R. and Weinmann, Andreas and Franke, Kai and Koch, Tobias},
booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection},
year={2025},
volume={},
number={},
pages={7637-7647},
doi={10.1109/WACV61041.2025.00742}}

SynDroneVision uses Unreal® Engine. Unreal® is a trademark or registered trademark of Epic Games, Inc. in the United States of America and elsewhere.