Method

RBNet [RBNet]


Submitted on 5 Feb. 2017 07:53 by
Zhe Chen (The University of Sydney)

Running time:0.18 s
Environment:GPU @ 2.5 Ghz (Matlab + C/C++)

Method Description:
Road Detection with the Res50 pre-trained
model.
Parameters:
lr:1e-2; epoch: 10k
Latex Bibtex:
@inproceedings{chen2017rbnet,
title={RBNet: A Deep Neural Network for Unified Road and
Road Boundary Detection},
author={Chen, Zhe and Chen, Zijing},
booktitle={International Conference on Neural Information
Processing},
pages={677--687},
year={2017},
organization={Springer}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 94.77 % 91.42 % 95.16 % 94.37 % 2.19 % 5.63 %
UMM_ROAD 96.06 % 93.49 % 95.80 % 96.31 % 4.64 % 3.69 %
UU_ROAD 93.21 % 89.18 % 92.81 % 93.60 % 2.36 % 6.40 %
URBAN_ROAD 94.97 % 91.49 % 94.94 % 95.01 % 2.79 % 4.99 %
UM_LANE 90.54 % 82.03 % 94.92 % 86.56 % 0.82 % 13.44 %
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 99.24 % 99.33 % 99.21 % 98.74 % 97.34 % 95.92 % 95.56 % 87.21 % 81.58 %
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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