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[bibtex]@InProceedings{Lenhard_2025_WACV, author = {Lenhard, Tamara R. and Weinmann, Andreas and Franke, Kai and Koch, Tobias}, title = {SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {7626-7636} }
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 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 can be accessed at https://zenodo.org/records/13360116.
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