An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy

Maciel-Pearson, B.G.; Breckon, T.P.

Abstract

Autonomous flight within a forest canopy represents a key challenge for generalised scene understanding on-board a future Unmanned Aerial Vehicle (UAV) platform. Here we present an approach for automatic trail navigation within such an environment that successfully generalises across differing image resolutions - allowing UAV with varying sensor payload capabilities to operate equally in such challenging environmental conditions. Specifically, this work presents an optimised deep neural network architecture, capable of stateof-the-art performance across varying resolution aerial UAV imagery, that improves forest trail detection for UAV guidance even when using significantly low resolution images that are representative of low-cost search and rescue capable UAV platforms.

Citation

Maciel-Pearson, B., & Breckon, T. (2017, December). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. Presented at The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us., Bristol, England

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us.
• Start Date: Dec 12, 2017
• Publication Date: 2017
• Deposit Date: Dec 5, 2017
• Publicly Available Date: Dec 6, 2017
• Pages: 1-3
• Book Title: Proc. Conf. on Robotics and Autonomous Systems - Robots that Work Among Us Workshop
• Keywords: drone, deep learning, convolutional neural network, robot guidance, flight guidance, unmanned aerial vehicle, unmanned aerial system, monocular, pathway detection
• Public URL: https://durham-repository.worktribe.com/output/1146113
• Publisher URL: https://breckon.org/toby/publications/papers/pearson17forest.pdf
• Related Public URLs: http://community.dur.ac.uk/toby.breckon/publications/papers/pearson17forest.pdf