Method

CLRD [la] [CLRD]


Submitted on 6 Oct. 2022 09:43 by
Martín Bayón (Universidad de León)

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Please, read our conference paper for further
details
Parameters:
Please, read our conference paper for further
details
Latex Bibtex:
@InProceedings{10.1007/978-3-031-15471-3_36,
author="Bayón-Gutiérrez, Martín and Benítez-
Andrades, José Alberto and Rubio-Martín, Sergio
and Aveleira-Mata, Jose and Alaiz-Moretón, Héctor
and García-Ordás, María Teresa",
title="Roadway Detection Using Convolutional
Neural Network Through Camera and LiDAR Data",
booktitle="Hybrid Artificial Intelligent Systems",
year="2022",
publisher="Springer International Publishing",
pages="419--430",
doi = {10.1007/978-3-031-15471-3_36}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 94.06 % 92.13 % 94.32 % 93.80 % 2.57 % 6.21 %
UMM_ROAD 95.41 % 94.83 % 95.23 % 95.59 % 5.26 % 4.41 %
UU_ROAD 92.41 % 90.16 % 92.30 % 92.52 % 2.52 % 7.48 %
URBAN_ROAD 94.20 % 92.66 % 94.25 % 94.14 % 3.16 % 5.86 %
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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