Method

FusionNet[st] [FusionNet]


Submitted on 31 Mar. 2017 15:02 by
Mingyi Lei (Xiamen University)

Running time:0.3 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
DeepLab in VGG16
RGB+Disparity
CRF
Parameters:
VGG16
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 94.15 % 92.26 % 95.18 % 93.14 % 2.15 % 6.86 %
UMM_ROAD 96.01 % 94.38 % 95.22 % 96.81 % 5.34 % 3.19 %
UU_ROAD 92.89 % 90.69 % 92.75 % 93.03 % 2.37 % 6.97 %
URBAN_ROAD 94.67 % 92.54 % 94.73 % 94.61 % 2.90 % 5.39 %
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.



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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.


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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