A Deep Neural Network Approach for Drogue Detection Using Laboratory-Chroma Key Images
Dillon Miller, Sean P. McCormick, Violet Mwaffo, Donald H. Costello
Abstract
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.