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Towards Robust Semantic Segmentation using Deep Fusion
Abhinav Valada, Gabriel L. Oliveira, Thomas Brox, and Wolfram Burgard
Robotics: Science and Systems (RSS 2016) Workshop, Are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics
2016
valada16rssws.pdf



Notes:
Robust semantic scene understanding of unstructured environments is critical for robots operating in the real world. Several inherent natural factors such as shadows, glare and snow make this problem highly challenging, especially using RGB images. In this paper, we propose the use of multispectral and multimodal images to increase robustness of segmentation in real-world outdoor environments. Deep Convolutional Neural Network (DCNN) architectures define the state of the art in various segmentation tasks. However, architectures that incorporate fusion have not been sufficiently explored. We introduce early and late fusion architectures for dense pixel-wise segmentation from RGB, Near-InfraRed (NIR) channels, and depth data. We identify data augmentation strategies that enable training of very deep fusion models using small datasets. We qualitatively and quantitatively evaluate our approach and show it exceeds several other state-of-the-art architectures. In addition, we present experimental results for segmentation under challenging realworld conditions. The dataset and demos are publicly available at http://deepscene.cs.uni-freiburg.de.


BibTeX:
@inproceedings{valada16rssws,
  author = {Abhinav Valada and Gabriel Oliveira and Thomas Brox and Wolfram Burgard},
  title = {Towards Robust Semantic Segmentation using Deep Fusion},
  booktitle = {Robotics: Science and Systems (RSS 2016) Workshop, Are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics},
  year = 2016,
  month = jun,
  url = {http://ais.informatik.uni-freiburg.de/publications/papers/valada16rssws.pdf},
  address = {Ann Arbor, USA}
}