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Efficient Deep Models for Monocular Road Segmentation
Gabriel L. Oliveira, Wolfram Burgard, Thomas Brox IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016) 2016
oliveira16iros.pdf
Notes: This paper addresses the problem of road scene
segmentation in conventional RGB images by exploiting recent
advances in semantic segmentation via convolutional neural
networks (CNNs). Segmentation networks are very large and
do not currently run at interactive frame rates. To make this
technique applicable to robotics we propose several architecture
refinements that provide the best trade-off between segmentation
quality and runtime. This is achieved by a new mapping
between classes and filters at the expansion side of the network.
The network is trained end-to-end and yields precise road/lane
predictions at the original input resolution in roughly 50ms.
Compared to the state of the art, the network achieves top
accuracies on the KITTI dataset for road and lane segmentation
while providing a 20× speed-up. We demonstrate that the
improved efficiency is not due to the road segmentation task.
Also on segmentation datasets with larger scene complexity, the
accuracy does not suffer from the large speed-up.
BibTeX:
@inproceedings{oliveira2016iros,
author = {Gabriel Oliveira and Wolfram Burgard and Thomas Brox},
title = {Efficient Deep Methods for Monocular Road Segmentation},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)},
year = 2016,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/oliveira16iros.pdf},
address = {Daejeon, Korea}
}
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