Loughborough University
Browse
ICUAS16_BNFinal.pdf (820.22 kB)

Unmanned ground operations using semantic image segmentation through a Bayesian network

Download (820.22 kB)
conference contribution
posted on 2016-10-07, 10:48 authored by Matthew CoombesMatthew Coombes, Will Eaton, Wen-Hua ChenWen-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.

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.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

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

Pages

868 - 877

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.

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Acceptance date

2016-05-06

Publication date

2016

Notes

© 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.

ISBN

9781467393331

Language

  • en

Location

Washington, DC, USA

Usage metrics

    Loughborough Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC