We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Computer Science


Publication details for Professor Toby Breckon

Maciel-Pearson, B.G. & Breckon, T.P. (2017), An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy, The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us. Bristol, UK Robotics and Autonomous Systems Network, 19-23.

Author(s) from Durham


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.