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Identification and location technology of refueling taper sleeve based on deep learning

Identification and location technology of refueling taper sleeve based on deep learning In the process of aerial refueling test flight and autonomous aerial refueling, it is necessary to measure the high-precision motion parameters of the Taper sleeve relative to the oil-receiving probe to provide data for the docking process. In this paper, aiming at the problems of intelligent identification and tracking of aerial refueling targets and high-precision stereo vision positioning, a multi-layer convolutional neural network with visual characteristics was constructed by deep learning theory, and the recognition results of Taper sleeve were corrected by using frame regression algorithm, so as to improve the Taper sleeve positioning accuracy from three dimensions: identification and tracking, optical calibration and measurement and solution. In this paper, combined with the test and flight verification, the solution accuracy is better than 0.09%, the identification success rate is better than 98%, and the Taper sleeve positioning accuracy is better than2cm+0.15%*L, which accords with the positioning accuracy of the refueling taper sleeve in the flight test. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of SPIE SPIE

Identification and location technology of refueling taper sleeve based on deep learning

Hu, Guoxing; Liu, Haoguang
Proceedings of SPIE , Volume 12968 – Dec 18, 2023
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Publisher
SPIE
Copyright
COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
ISSN
0277-786X
eISSN
1996-756X
DOI
10.1117/12.3007578
Publisher site
See Article on Publisher Site

Abstract

In the process of aerial refueling test flight and autonomous aerial refueling, it is necessary to measure the high-precision motion parameters of the Taper sleeve relative to the oil-receiving probe to provide data for the docking process. In this paper, aiming at the problems of intelligent identification and tracking of aerial refueling targets and high-precision stereo vision positioning, a multi-layer convolutional neural network with visual characteristics was constructed by deep learning theory, and the recognition results of Taper sleeve were corrected by using frame regression algorithm, so as to improve the Taper sleeve positioning accuracy from three dimensions: identification and tracking, optical calibration and measurement and solution. In this paper, combined with the test and flight verification, the solution accuracy is better than 0.09%, the identification success rate is better than 98%, and the Taper sleeve positioning accuracy is better than2cm+0.15%*L, which accords with the positioning accuracy of the refueling taper sleeve in the flight test.

Journal

Proceedings of SPIE – SPIE

Published: Dec 18, 2023

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