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Spatio-temporal road detection from aerial imagery using CNNs

Author
Luque, B.; Morros, J.R.; Ruiz-Hidalgo, J.
Type of activity
Presentation of work at congresses
Name of edition
12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Date of publication
2017
Presentation's date
2017-02-27
Book of congress proceedings
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP
First page
493
Last page
500
Publisher
SCITEPRESS
DOI
https://doi.org/10.5220/0006128904930500 Open in new window
Repository
http://hdl.handle.net/2117/104723 Open in new window
URL
http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=Ki5fKyigC7Q=&t=1 Open in new window
Abstract
The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the...
Citation
Luque, B., Morros, J.R., Ruiz-Hidalgo, J. Spatio-temporal road detection from aerial imagery using CNNs. A: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. "Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP". Porto: SCITEPRESS, 2017, p. 493-500.
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

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