A Deep Neural Network Approach for Drogue Detection Using Laboratory-Chroma Key Images
This study presents a framework for developing and evaluating a deep neural network model trained on a synthetic dataset of aerial refueling equipment. The data set was generated in a controlled laboratory environment with green screen backgrounds. The model’s performance is rigorously co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10792906/ |
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| Summary: | This study presents a framework for developing and evaluating a deep neural network model trained on a synthetic dataset of aerial refueling equipment. The data set was generated in a controlled laboratory environment with green screen backgrounds. The model’s performance is rigorously compared to a counterpart trained on real-world data, revealing that the synthetic data approach not only offers a cost-effective alternative but also achieves comparable accuracy in identifying critical components for uncrewed aerial refueling missions. Despite minor classification errors, particularly with small, low-contrast objects, the results demonstrate the strong potential of synthetic data in advancing autonomous aerial refueling systems. |
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| ISSN: | 2169-3536 |