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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18165

Title: Image segmentation for automated taxiing of unmanned aircraft
Authors: Eaton, William H.
Chen, Wen-Hua
Keywords: Autonomy
Manned/unmanned aviation
See-and-avoid systems
Issue Date: 2015
Publisher: © IEEE
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.
Abstract: 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.
Description: © 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.
Sponsor: The authors would like to thank BAE Systems for their continued support throughout this project.
Version: Accepted for publication
DOI: 10.1109/ICUAS.2015.7152268
URI: https://dspace.lboro.ac.uk/2134/18165
Publisher Link: http://dx.doi.org/10.1109/ICUAS.2015.7152268
ISBN: 9781479960095
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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