Method

Fully Convolutional Neural Network with Global Context Blocks [FCN-GCBs]


Submitted on 11 Jan. 2017 02:35 by
ZhenWen Zhu (IMECAS)

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

Method Description:
Finetune.
Parameters:
Lr=1e-3
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 93.86 % 86.62 % 92.15 % 95.62 % 3.71 % 4.38 %
UMM_ROAD 95.29 % 91.27 % 95.16 % 95.43 % 5.33 % 4.57 %
UU_ROAD 92.10 % 83.69 % 89.61 % 94.73 % 3.58 % 5.27 %
URBAN_ROAD 94.08 % 87.66 % 92.87 % 95.32 % 4.03 % 4.68 %
UM_LANE 91.24 % 85.14 % 92.15 % 90.35 % 1.35 % 9.65 %
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 99.18 % 99.13 % 98.76 % 99.00 % 98.11 % 96.43 % 95.56 % 93.02 % 81.58 %
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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.



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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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