Automatic UAV forced landing site detection using machine learning

Guo, Xufeng, Denman, Simon, Fookes, Clinton B., Mejias, Luis, & Sridharan, Sridha (2014) Automatic UAV forced landing site detection using machine learning. In Bouzerdoum, Abdesselam, Wang, Lei, & Ogunbona, Philip (Eds.) Proceedings of the 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Wollongong, NSW, pp. 1-7.

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Abstract

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

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ID Code: 82223
Item Type: Conference Paper
Refereed: Yes
Keywords: Gaussian processes, Autonomous aerial vehicles, Feature extraction, Image colour analysis, Learning (artificial intelligence), Mixture models, Neural nets, Support vector machines
DOI: 10.1109/DICTA.2014.7008097
ISBN: 9781479954094
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2014 by IEEE
Deposited On: 06 Mar 2015 01:20
Last Modified: 12 Sep 2016 01:31

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