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

David James Warne, Ross Hayward, Neil Kelson, Jasmine Banks, Luis Mejias


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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Unmanned Aerial Vehicle; Emergency Landing; Pulse Coupled Neural Network; Feature Classification; Field Programmable Gate Array; OpenCL

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DOI: http://dx.doi.org/10.21914/anziamj.v55i0.7851

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