SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection

• URL: http://arxiv.org/abs/2411.05633v1
• Date: Fri, 08 Nov 2024 15:22:49 GMT
• Title: SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
• Authors: Tamara R. Lenhard, Andreas Weinmann, Kai Franke, Tobias Koch,
• Abstract summary: We present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. 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.
• Score: 3.9061053498250753
• License:
• 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 will be publicly released upon paper acceptance.

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