Image segmentation for automated taxiing of unmanned aircraft
conference contributionposted on 2015-07-07, 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.
The authors would like to thank BAE Systems for their continued support throughout this project.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inInternational Conference on Unmanned Aircraft Systems
CitationEATON, 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.
- AM (Accepted Manuscript)
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