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Unmanned ground operations using semantic image segmentation through a Bayesian network

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conference contribution
posted on 07.10.2016, 10:48 by Matthew Coombes, Will Eaton, Wen-Hua Chen
This paper discusses the machine vision element of a system designed to allow automated taxiing for Unmanned Aerial System (UAS) around civil aerodromes. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detect potential collision risks. This is achieved through the use of a singular monocular sensor. Untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are then used to estimate the class. As the competency of each individual estimate can vary based 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 map matching.


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.



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

Published in

International Conference on Unmanned Aircraft Systems 2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016


868 - 877


COOMBES, M., EATON, W.H. and CHEN, W.-H., 2016. Unmanned ground operations using semantic image segmentation through a Bayesian network. International Conference on Unmanned Aircraft Systems (ICUAS 2016), Arlington, VA USA, 7th-10th June 2016, pp. 868-877.




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