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

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.

Descriptions

Resource type

Dataset

Contributors

Creator: Maciel-Pearson, Bruna G. ¹
Creator: Breckon, Toby P. ¹

¹ Durham University

Funder

Engineering and Physical Sciences Research Council

Research methods

Other description

Keyword

Deep learning
Trail detection
Autonomous Unmanned Aerial Vehicle
Unstructured environment
Drone
Convolutional Neural Network
UAV

Subject

Robotics
Deep Learning
Monocular Vision
Computer Science
Machine Learning

Location

Durham, England, United Kingdom

Language

English

Cited in

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, pp. 1-11, 2018.

Identifier

ark:/32150/r1st74cq45z
doi:10.15128/r1st74cq45z

Rights

All rights reserved All rights reserved

Publisher

Durham University

Date Created

2018

File Details

Depositor

B.G. Maciel Pearson

Date Uploaded

4 May 2018, 13:05:15

Date Modified

4 May 2018, 13:05:23

Audit Status

Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 1798571204

Last modified: 2018:05:04 13:24:35+01:00

Filename: UAVTrailNavigationDataset-Pearson-Breckon-2018-1211-0305-version3.zip

Original checksum: e4781cdfeb6cedbc9673de51c3c67094