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Image segmentation for automated taxiing of unmanned aircraft

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conference contribution
posted on 07.07.2015, 12:56 authored by William H. Eaton, Wen-Hua ChenWen-Hua Chen
This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation prior to classification is suggested as a better method for accurate region border extraction. This is achieved through an initial over-segmentation using the established SLIC superpixel technique with further untrained clustering using DBSCAN algorithm. Known classes are used to train a classifier through construction of a texton dictionary and models of texton content typical to each class. The paper demonstrates the application of said system to real world images, and shows good automated segment identification. Remaining issues are identified and contextual information is suggested as a method of resolving them going forward.

Funding

The authors would like to thank BAE Systems for their continued support throughout this project.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Conference on Unmanned Aircraft Systems

Citation

EATON, W.H. and CHEN, W.-H., 2015. Image segmentation for automated taxiing of unmanned aircraft. Presented at: The 2015 International Conference on Unmanned Aircraft Systems, ICUAS'15, 9th-12th June 2015, Denver, Colorado, USA, pp.1-8.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2015

Notes

© 2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

9781479960095

Language

en

Location

Denver, Colorado, USA

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