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

Hayward, Ross F., Warne, David, Kelson, Neil A., Banks, Jasmine, & Mejias, Luis (2013) Pulse-coupled neural network performance for real-time identification of vegetation during forced landing. In 11th Engineering Mathematics and Applications Conference, 1-4 December 2013, Queensland University of Technology, Brisbane, QLD. (Unpublished)

Abstract

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and 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 obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

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ID Code: 65474
Item Type: Conference Item (Presentation)
Refereed: Yes
Additional URLs:
Keywords: Unmanned Aerial Vehicle, Emergency landing, Pulse Coupled Neural Network, 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 > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000) > COMPUTER HARDWARE (100600) > Logic Design (100603)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000) > COMPUTER HARDWARE (100600) > Performance Evaluation; Testing and Simulation of Reliability (100605)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000) > COMPUTER HARDWARE (100600) > Processor Architectures (100606)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > QUT Faculties and Divisions > Division of Technology, Information and Library Services
Current > Research Centres > High Performance Computing and Research Support
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 The Authors
Deposited On: 06 Mar 2014 01:18
Last Modified: 22 Jun 2017 14:46

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