Pulse-coupled neural network performance for real-time identification of vegetation during forced landing
Warne, David James , Hayward, Ross , Kelson, Neil , Banks, Jasmine, & Mejias, Luis (2014) Pulse-coupled neural network performance for real-time identification of vegetation during forced landing. ANZIAM Journal, 55, c1-c16.
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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|Item Type:||Journal Article|
|Keywords:||Unmanned Aerial Vehicle, Emergency Landing, Pulse Coupled Neural Network, Feature Classification, Field Programmable Gate Array, OpenCL|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Avionics (090105)
|Divisions:||Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support
Current > Schools > School of Electrical Engineering & Computer Science
Current > Research Centres > High Performance Computing and Research Support
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||24 Mar 2014 03:08|
|Last Modified:||12 Sep 2016 01:32|
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