Method

Shadow Region Detection Assisted Lane Detection [st] [SAFE]


Submitted on 8 Jul. 2020 07:22 by
Chun Zhe Lim (Heriot-Watt University Malaysia)

Running time:0.06 s
Environment:2 cores @ 3.0 Ghz (Python + C/C++)

Method Description:
Vision-based lane detection method is susceptible
to interference from challenging illumination
scenarios. A novel single-image shadow detection
method is proposed to improve feature extraction
of lane markings in challenging illumination
scenario such as shadowed environment. Shadowed
region is identified in order to adaptively apply
the threshold of edge detector to shadowed and
non-shadowed regions. Thus, improving lane
marking feature extraction.
Parameters:
CS1=0.5370234753018429
CS2=0.4473392833717158
CNS1=0.44517492633842215
CNS2=1.3145589168065772
nS=6
nNS=5
wS=5
wNS=7
minLinLength=19
maxLinGap=51
TH=24
rho=1.4061982292723891
Δwidth=235
γ=1.1033772213555915
δ=0.13004374298465363
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_LANE 73.72 % 58.54 % 68.19 % 80.23 % 6.59 % 19.77 %
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Behavior Evaluation


Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40
UM_LANE 98.68 % 97.88 % 95.73 % 98.12 % 94.95 % 89.77 % 90.48 % 76.25 % 55.07 %
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.



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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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