The KITTI Vision Benchmark Suite

Method

SNE-RoadSeg+ [SNE-RoadSeg+]

Submitted on 26 Feb. 2021 13:07 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:

@inproceedings{wang2021sne,
title = {{SNE-RoadSeg+}: Rethinking
depth-normal translation and deep supervision for
freespace detection},
author = {Wang, Hengli and Fan, Rui and
Cai, Peide and Liu, Ming},
booktitle = {2021 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS)},
pages = {},
year = {2021},
organization = {IEEE},
doi = {}
}

Evaluation in Bird's Eye View

Benchmark	MaxF	AP	PRE	REC	FPR	FNR
UM_ROAD	96.95 %	93.60 %	96.99 %	96.90 %	1.37 %	3.10 %
UMM_ROAD	98.13 %	95.52 %	98.01 %	98.25 %	2.19 %	1.75 %
UU_ROAD	97.04 %	92.97 %	96.84 %	97.24 %	1.03 %	2.76 %
URBAN_ROAD	97.50 %	93.98 %	97.41 %	97.58 %	1.43 %	2.42 %

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.

This figure as: png eps pdf

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.

This figure as: png