Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy - Evaluation [dataset]

Autonomous flight within a forest canopy represents a key challenge for generalised scene understanding on-board a future Unmanned Aerial Vehicle (UAV) platforms. Here we present an approach for automatic trail navigation within such an unstructured environment that successfully generalises across differing image resolutions - allowing UAV with a varying sensor payload capabilities to operate equally in such challenging environmental conditions. Specifically, this work presents an optimised deep neural network architecture, capable of state-of-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. Used in the paper: Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy (B.G. Maciel-Pearson, P. Carbonneau, T.P. Breckon), In Proc. Towards Autonomous Robotic Systems Conference, Springer, 2018.