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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.
- Resource type
Maciel-Pearson, Bruna G.
Creator: Breckon, Toby P. 1
1 Durham University
Engineering and Physical Sciences Research Council
- Research methods
- Other description
- Deep learning
Autonomous Unmanned Aerial Vehicle
Convolutional Neural Network
Durham, England, United Kingdom
- 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.
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- Date Created
- B.G. Maciel Pearson
- Date Uploaded
- 4 May 2018, 13:05:15
- Date Modified
- 4 May 2018, 13:05:23
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File format: zip (ZIP Format)
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Last modified: 2018:05:04 13:24:35+01:00
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