Method

Siamesed Fully Convolutional Networks with Location Priors [s-FCN-loc]
[Anonymous Submission]

Submitted on 14 Sep. 2016 13:26 by
[Anonymous Submission]

Running time:0.4 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Siamesed Fully Convolutional Networks with
Location Priors
Parameters:
learning rate = e^-10
Latex Bibtex:
\@inproceedings{gao2017conf_embedding,
title={Embedding structured contour and location
prior in siamesed fully convolutional networks for
road detection},
author={Gao, Junyu and Wang, Qi and Yuan, Yuan},
booktitle={Robotics and Automation (ICRA), 2017
IEEE International Conference on},
pages={219--224},
year={2017},
organization={IEEE}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 92.85 % 87.41 % 93.02 % 92.68 % 3.17 % 7.32 %
UMM_ROAD 95.01 % 91.86 % 95.81 % 94.23 % 4.53 % 5.77 %
UU_ROAD 90.42 % 80.07 % 92.40 % 88.52 % 2.37 % 11.48 %
URBAN_ROAD 93.26 % 88.83 % 94.16 % 92.39 % 3.16 % 7.61 %
This table as LaTeX

Behavior Evaluation


This table as LaTeX

Road/Lane Detection

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


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.


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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