Method

DFM-RTFNet [DFM-RTFNet]


Submitted on 26 Dec. 2020 17:02 by
Hengli Wang (Hong Kong University of Sciense and Techonology)

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

Method Description:
TBA
Parameters:
TBA
Latex Bibtex:
@article{wang2021dynamic,
title = {Dynamic fusion module evolves
drivable area and road anomaly detection: A
benchmark and algorithms},
author = {Wang, Hengli and Fan, Rui and Sun,
Yuxiang and Liu, Ming},
journal = {IEEE Transactions on Cybernetics},
year = {2021},
publisher = {IEEE},
doi = {10.1109/TCYB.2021.3064089}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 96.46 % 93.66 % 96.58 % 96.33 % 1.55 % 3.67 %
UMM_ROAD 97.45 % 95.63 % 97.33 % 97.58 % 2.94 % 2.42 %
UU_ROAD 96.26 % 93.01 % 96.16 % 96.35 % 1.25 % 3.65 %
URBAN_ROAD 96.78 % 94.05 % 96.62 % 96.93 % 1.87 % 3.07 %
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
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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