Spatio-temporal road detection from aerial imagery using CNNs
Visualitza/Obre
Cita com:
hdl:2117/104723
Tipus de documentText en actes de congrés
Data publicació2017
EditorSCITEPRESS
Condicions d'accésAccés obert
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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 videos in this database. The
experimental results using our modified version of SegNet show a big improvement on the performance of the
neural network when using aerial imagery, obtaining over 90% accuracy.
CitacióLuque, 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.
ISBN978-989-758-225-7
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cLuque17.pdf | Main article | 6,138Mb | Visualitza/Obre |