Method

Up Convolutional Network [Up-Conv-Poly]
https://lmb.informatik.uni-freiburg.de/resources/binaries/PartSeg/caffe_FAST.tar.gz

Submitted on 9 Feb. 2016 17:17 by
Gabriel Oliveira (University of Freiburg)

Running time:0.08 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Up convolutional network with higher resolution
output and poly training.
Parameters:
Lr=1e-10 to 1e-8.
Latex Bibtex:
@inproceedings{Oliveira2016IROS,
author = {Gabriel Oliveira and Wolfram Burgard
and Thomas Brox},
title = {Efficient Deep Methods for Monocular
Road Segmentation},
journal = {IROS},
year = {2016},
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 92.20 % 88.85 % 92.57 % 91.83 % 3.36 % 8.17 %
UMM_ROAD 95.52 % 92.86 % 95.37 % 95.67 % 5.10 % 4.33 %
UU_ROAD 92.65 % 89.20 % 92.85 % 92.45 % 2.32 % 7.55 %
URBAN_ROAD 93.83 % 90.47 % 94.00 % 93.67 % 3.29 % 6.33 %
UM_LANE 89.88 % 87.52 % 92.01 % 87.84 % 1.34 % 12.16 %
This table as LaTeX

Behavior Evaluation


Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40
UM_LANE 99.06 % 98.84 % 98.45 % 97.57 % 95.27 % 93.14 % 90.11 % 83.72 % 77.63 %
This table as LaTeX

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.



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Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.



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Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.



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