Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data
Mariusz Wisniewski, Zeeshan A. Rana, Ivan Petrunin, Alan Holt, Stephen, Harman

TL;DR
This paper demonstrates that a drone detection model trained solely on synthetic data can effectively transfer to real-world scenarios, achieving high accuracy and reducing data collection costs.
Contribution
The study introduces a synthetic-data-trained Faster-RCNN model for drone detection that performs comparably to real-data models, addressing the sim-to-real transfer challenge.
Findings
Achieves 97.0% AP_50 on real drone dataset
Synthetic data reduces labeling costs
Potential for safety-critical applications
Abstract
Drone detection has benefited from improvements in deep neural networks, but like many other applications, suffers from the availability of accurate data for training. Synthetic data provides a potential for low-cost data generation and has been shown to improve data availability and quality. However, models trained on synthetic datasets need to prove their ability to perform on real-world data, known as the problem of sim-to-real transferability. Here, we present a drone detection Faster-RCNN model trained on a purely synthetic dataset that transfers to real-world data. We found that it achieves an AP_50 of 97.0% when evaluated on the MAV-Vid - a real dataset of flying drones - compared with 97.8% for an equivalent model trained on real-world data. Our results show that using synthetic data for drone detection has the potential to reduce data collection costs and improve labelling…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Infrared Target Detection Methodologies
Methods1x1 Convolution
