Spatio-temporal road detection from aerial imagery using CNNs
Document typeConference report
Rights accessOpen Access
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.
CitationLuque, 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.