Spatio-temporal Road Detection from Aerial Imagery using CNNs

Belén Luque, Josep Ramon Morros, Javier Ruiz-Hidalgo

2017

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.

Paper Citation

in Harvard Style

Luque B., Morros J. and Ruiz-Hidalgo J. (2017). Spatio-temporal Road Detection from Aerial Imagery using CNNs . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 493-500. DOI: 10.5220/0006128904930500

in Bibtex Style

@conference{visapp17,
author={Belén Luque and Josep Ramon Morros and Javier Ruiz-Hidalgo},
title={Spatio-temporal Road Detection from Aerial Imagery using CNNs},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={493-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006128904930500},
isbn={978-989-758-225-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Spatio-temporal Road Detection from Aerial Imagery using CNNs
SN - 978-989-758-225-7
AU - Luque B.
AU - Morros J.
AU - Ruiz-Hidalgo J.
PY - 2017
SP - 493
EP - 500
DO - 10.5220/0006128904930500