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Roadmap Generation Using a Multi-Stage Ensemble of Deep Neural Networks With Smoothing-Based Optimization
AbstractRoad detection from aerial images is a challenging task for humans and machines alike. Occlusion, the lack of visual cues and slim class borders for other road-like structures (such as pathways or private alleys) make the problem inherently ambiguous, requiring logic that goes beyond the input image. We propose a three-stage method for the task of road segmentation - first, an ensemble of multiple U-Net like CNNs generate binary road masks. Second, an optimization algorithm generates road vectors with their corresponding thickness based on the fusion of the road maps from the first stage. Third, missing links are added based on the inferred graph to improve segmentation.
Related Material
[pdf][bibtex]@InProceedings{Costea_2018_CVPR_Workshops,
author = {Costea, Dragos and Marcu, Alina and Slusanschi, Emil and Leordeanu, Marius},
title = {Roadmap Generation Using a Multi-Stage Ensemble of Deep Neural Networks With Smoothing-Based Optimization},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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