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Deep Multispectral Semantic Scene Understanding of Forested Environments using Multimodal Fusion
Abhinav Valada, Gabriel L. Oliveira, Thomas Brox, Wolfram Burgard
The 2016 International Symposium on Experimental Robotics (ISER 2016)
2016
valada16iser.pdf
Notes:
Semantic scene understanding of unstructured environments
is a highly challenging task for robots operating in the real world. Deep
Convolutional Neural Network architectures define the state of the art
in various segmentation tasks. So far, researchers have focused on segmentation
with RGB data. In this paper, we study the use of multispectral
and multimodal images for semantic segmentation and develop
fusion architectures that learn from RGB, Near-InfraRed channels, and
depth data. We introduce a first-of-its-kind multispectral segmentation
benchmark that contains 15, 000 images and 366 pixel-wise ground truth
annotations of unstructured forest environments. We identify new data
augmentation strategies that enable training of very deep models using
relatively small datasets. We show that our UpNet architecture exceeds
the state of the art both qualitatively and quantitatively on our benchmark.
In addition, we present experimental results for segmentation under
challenging real-world conditions. Benchmark and demo are publicly
available at http://deepscene.cs.uni-freiburg.de.
BibTeX:
@inproceedings{valada16iser,
author = {Abhinav Valada and Gabriel Oliveira and Thomas Brox and Wolfram Burgard},
title = {Deep Multispectral Semantic Scene Understanding of Forested Environments using Multimodal Fusion},
booktitle = {The 2016 International Symposium on Experimental Robotics (ISER 2016)},
year = 2016,
month = oct,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/valada16iser.pdf},
address = {Tokyo, Japan}
}
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