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Machine vision for UAS ground operations: using semantic segmentation with a bayesian network classifier

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journal contribution
posted on 23.05.2017, 13:24 by Matthew CoombesMatthew Coombes, Will EatonWill Eaton, Wen-Hua ChenWen-Hua Chen
This paper discusses the machine vision element of a system designed to allow Unmanned Aerial System (UAS) to perform automated taxiing around civil aerodromes, with only a monocular camera. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detecting potential collision risks. In practice, untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are used to estimate the class. As the competency of each individual estimate can vary dependent on multiple factors (number of pixels, lighting conditions and even surface type). A Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for localisation and obstacle detection.

Funding

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner. The work greatly benefits from the data set collected from an airfield provided by BAE Systems and technical advice provided by the technical officer Rob Buchanan.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Journal of Intelligent and Robotic Systems: Theory and Applications

Pages

1 - 20

Citation

COOMBES, M., EATON, W.H. and CHEH, W.-H., 2017. Machine vision for UAS ground operations: using semantic segmentation with a bayesian network classifier. Journal of Intelligent and Robotic Systems, 88(2-4), pp. 527-546.

Publisher

Springer / © The Authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

16/03/2017

Publication date

2017

Notes

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0921-0296

eISSN

1573-0409

Language

en