# SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection

arXiv: https://arxiv.org/abs/2411.05633 · HTML: https://mlsanity.com/paper/2411.05633/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [TAM: Temporal Adaptive Module for Video Recognition](https://mlsanity.com/paper/2005.06803/) (2020)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images](https://mlsanity.com/paper/2405.20469/) (2024)
- [WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models](https://mlsanity.com/paper/2305.07528/) (2023)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [A Deep Learning Approach to Drone Monitoring](https://mlsanity.com/paper/1712.00863/) (2017)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [A dataset for multi-sensor drone detection](https://mlsanity.com/paper/2111.01888/) (2021)
- [DDOS: The Drone Depth and Obstacle Segmentation Dataset](https://mlsanity.com/paper/2312.12494/) (2023)
- [Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks](https://mlsanity.com/paper/1812.08333/) (2018)
- [Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts](https://mlsanity.com/paper/2305.16450/) (2023)
- [YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection](https://mlsanity.com/paper/2406.11641/) (2024)
- [Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory](https://mlsanity.com/paper/2107.00962/) (2021)
- [SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions](https://mlsanity.com/paper/2311.14764/) (2023)

## Built on by (most cited first, 8 of 8)

- [Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation](https://mlsanity.com/paper/2504.19347/) (2025)
- [Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges](https://mlsanity.com/paper/2507.10006/) (2025)
- [Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments](https://mlsanity.com/paper/2509.14012/) (2025)
- [Detector-Augmented SAMURAI for Long-Duration Drone Tracking](https://mlsanity.com/paper/2601.04798/) (2026)
- [LRDDv3: High-Resolution Long-Range Drone Detection Dataset with Range Information and Thermal Data](https://mlsanity.com/paper/2605.25942/) (2026)
- [SimD3: A Synthetic drone Dataset with Payload and Bird Distractor Modeling for Robust Detection](https://mlsanity.com/paper/2601.14742/) (2026)
- [Beyond Clear Skies: Synthetic Seasonal and Weather Variations for Real-World Drone Detection](https://mlsanity.com/paper/2608.16191/) (2026)
- [DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models](https://mlsanity.com/paper/2609.07780/) (2026)

## Abstract (arXiv)

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, leveraging synthetic data generated via game engine-based simulations provides 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 demonstrate 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. SynDroneVision will be publicly released upon paper acceptance.