Thesis
Dataset augmentation pipeline for improved long-range drone detection
Master of Science (M.S.), Drexel University
Dec 2023
DOI:
https://doi.org/10.17918/00001898
Abstract
This thesis addresses the critical need for accurate drone detection models in response to the widespread use of Unmanned Aerial Vehicles (UAVs) across various industries which introduces safety risks and security concerns, highlighting the importance of developing robust drone detection algorithms. To address these challenges, this thesis presents a two-phase solution for long-range drone detection. The first phase focuses on seamlessly integrating synthetic data, generated with Unreal Engine, with real-world data to enhance dataset diversity. In the subsequent phase, the Cycle-consistent Generative Adversarial Network (CycleGAN) model is employed to translate synthetic images into the real domain, effectively bridging the gap between synthetic and real data. This comprehensive approach aims to advance drone detection models in terms of precision while also providing a cost-effective and accessible method for generating diverse drone detection datasets. To assess the effectiveness of the proposed pipeline for data augmentation, the You Only Look Once (YOLO) model is trained in three scenarios: using only real data, augmenting real data with synthetic data, and augmenting real data with translated data. The trained models are then tested on three unseen real datasets: DetFly, Drone Detection, and UAV Detect. The results conclusively demonstrate that including both synthetic and translated images in the real dataset enhances drone detection capabilities. Notably, the most substantial improvement is observed when augmenting real data with translated data. These findings emphasize the significance of the proposed approach in enhancing drone detection performance.
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Details
- Title
- Dataset augmentation pipeline for improved long-range drone detection
- Creators
- Solmaz Arezoomandan
- Contributors
- David Han (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- 48 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering (1970-2026); Drexel University
- Other Identifier
- 991021823113204721