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Title: Unmanned ground operations using semantic image segmentation through a Bayesian network
Authors: Coombes, Matthew
Eaton, William H.
Chen, Wen-Hua
Keywords: Unmanned ground operations
Semantic image segmentation
Bayesian network
Domain knowledge
Issue Date: 2016
Publisher: © IEEE
Citation: 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.
Abstract: 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.
Description: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: 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.
Version: Accepted for publication
DOI: 10.1109/ICUAS.2016.7502572
URI: https://dspace.lboro.ac.uk/2134/22727
Publisher Link: http://dx.doi.org/10.1109/ICUAS.2016.7502572
ISBN: 9781467393331
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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