Summary of Syndronevision: a Synthetic Dataset For Image-based Drone Detection, by Tamara R. Lenhard et al.
SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
by Tamara R. Lenhard, Andreas Weinmann, Kai Franke, Tobias Koch
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary SynDroneVision is a synthetic dataset designed to train deep learning algorithms for RGB-based drone detection in surveillance applications. The dataset features diverse backgrounds, lighting conditions, and drone models, providing a comprehensive training foundation for robust drone detection systems. To evaluate SynDroneVision’s effectiveness, the authors perform a comparative analysis across recent YOLO detection models, demonstrating notable enhancements in model performance and robustness while reducing time and costs of real-world data acquisition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data generated via game engine-based simulations can help overcome limitations of large-scale annotated training data for drone detection. The paper presents SynDroneVision, a synthetic dataset designed specifically for RGB-based drone detection in surveillance applications. It offers diverse backgrounds, lighting conditions, and drone models to train deep learning algorithms. The authors compare different YOLO detection models using this dataset and show it improves model performance and robustness while reducing costs. |
Keywords
» Artificial intelligence » Deep learning » Synthetic data » Yolo
