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Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

D.J. Warne, R. Hayward, N. Kelson, J. Banks, L. Mejias
High Performance Computing and Research Support, Queensland University of Technology, Queensland 4001, Australia; School of Electrical Engineering and Computer Science, Queensland University of Technology, Queensland 4001, Australia; Australian Research Center for Aerospace Automation, Queensland University of Technology, Queensland 4001, Australia
ANZIAM J. 55 (EMAC2013) pp.C1–C16, 2014

@article{warne2014pulse,

   title={Pulse-coupled neural network performance for real-time identification of vegetation during forced landing},

   author={Warne, David James and Hayward, Ross and Kelson, Neil and Banks, Jasmine and Mejias, Luis},

   journal={ANZIAM Journal},

   volume={55},

   pages={C1–C16},

   year={2014}

}

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Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.
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