Using Deep Learning for the Dynamic Evaluation of Road Marking
Features from Laser Imaging
Maxime Tual, Val
erie Muzet
, Philippe Foucher
, Christophe Heinkel
and Pierre Charbonnier
Research Team ENDSUM, Cerema, 11 rue Jean Mentelin, 67035 Strasbourg, France
Road Marking, Deep Learning, Regression, Evaluation, Laser Data, LCMS Sensor.
Road markings are essential guidance elements for both drivers and driver assistance systems: their mainte-
nance requires regularly scheduled performance surveys. In this paper, we introduce a deep learning based
method to estimate two indicators of the quality of road markings (the percentage of remaining marking and
the contrast) directly from their appearance, using reflectance data acquired by a mobile laser imaging system
used for inspections. To do this, we enhance the EfficientDet architecture by adding an output sub-network to
predict the indicators. It is not possible to physically establish large-scale reference measurements for train-
ing and testing our model, but this can be done indirectly by semi-supervised image annotation, a strategy
validated by our experiments. Our results show that it is advisable to train the model end-to-end without op-
timizing its detection performance. They also enlighten the very good accuracy of the indicators predicted by
the model.
In this contribution, we introduce a method, based on
an end-to-end trainable deep learning model, for eval-
uating quality metrics directly from the visual appear-
ance of road markings, as illustrated in fig. 1.
Figure 1: Our method provides a bounding box surround-
ing the marking, and an estimate of its quality, without any
intermediate processing.
Road markings provide both visibility and visual
guidance to the driver and, nowadays, to advanced
driver assistance systems (ADAS) and autonomous
vehicles. Maintaining markings in good condition is
therefore fundamental for road safety, and this implies
regular inspections. To carry out measurements of
marking quality, we consider here two complemen-
tary metrics, that are related to the visual quality of
markings in daytime, namely the percentage of resid-
ual marking (PRM), which characterizes the level of
wear of the paint itself, and the contrast between the
remaining painted surface and the surrounding pave-
In this work, we use a laser imaging system, po-
sitioned vertically to the road. It provides an image
of the intensity reflected from the pavement and the
markings, independent of the external conditions, in a
geometry favorable to PRM and contrast estimation.
Whereas many methods have been developed over
the last decades to detect road markings for vehicle
guidance purposes, few works have been concerned
with the evaluation of the quality of markings from
images. The commonly used approach is to perform
a segmentation of the markings (i.e. to estimate a map
of pixel membership to the marking) and to compute
the metrics from it. The drawback of this approach
is that it usually involves setting a series of parame-
ters, and that errors can propagate from one process-
ing step to the next, with a strong impact on the final
We postulate that segmentation is not necessary,
and that it should be possible to infer the quality mea-
sures of the marking directly from the images. This
is a regression task, certainly highly dimensional and
non-linear, but which deep learning techniques are
nowadays able to handle. Of course, it is necessary
Tual, M., Muzet, V., Foucher, P., Heinkelé, C. and Charbonnier, P.
Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging.
DOI: 10.5220/0012595600003720
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 4th International Conference on Image Processing and Vision Engineering (IMPROVE 2024), pages 23-31
ISBN: 978-989-758-693-4; ISSN: 2795-4943
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
to locate in the image the marking element that corre-
sponds to a given measurement, surrounding it with a
bounding box. This is a detection task, another prime
application for deep learning.
We have therefore modified an efficient neural ar-
chitecture dedicated to object detection in images, by
adapting it to the case of road markings and by adding
a sub-network allowing the inference of the sought in-
dicators. To carry out the training of this composite
network and the evaluation of its predictions, a large
quantity of images was collected and annotated by an
operator in a semi-supervised fashion. We show the
promising results obtained by this novel method.
The rest of the paper is organized as follows. In
sec. 2, we propose a brief review of existing related
works. Then, we describe in sec. 3 how we collected
the data needed to train and evaluate our method.
Sec. 4 is dedicated to methodological aspects related
to the annotation of markings, the architecture of the
proposed deep learning model and its training. Ex-
perimental results are presented in sec. 5, and sec. 6
concludes the paper.
In this paper, we aim at evaluating the visual qual-
ity of the markings, with characteristics that can
be related to the performance of both human vi-
sion and automated vehicle (AV) systems, in day-
light. It was shown that the contrast between mark-
ing and surrounding road surface is a good candidate
for this (Carlson and Poorsartep, 2017) and there-
fore it is one of our parameters of interest. We
moreover consider the percentage of residual marking
(PRM), which also conditions the visibility of mark-
ings. Even if the latter is defined in several coun-
tries, like UK (CS 126, 2022), Korea (Lee and Cho,
2023), Germany (Mesenberg, 2003) and France (NF
EN 1824, 2020), it often appears in the form of
a score, quantized according to wear level classes,
called cover index. Moreover, the standardized mea-
surement geometry (2.29
observation angle) is too
grazing to evaluate it precisely and, to our knowledge,
there is no operational PRM measurement system, un-
til now. We propose an answer to this need, thanks
to an imaging device that acquires images at traffic
speed, under a 90
angle, and to a method that esti-
mates a PRM figure, not a wear level class.
Road marking detection has been investigated for
more than 40 years with the aim of designing ADAS,
in particular lane keeping systems: a complete survey
of the topic is therefore out of the scope of this pa-
per. Note that processing methods have been devel-
oped for passive acquisition systems (supplying im-
ages) (Zhang et al., 2022) as well as for active ones
(using laser scanners or retroreflectometers) (Zhang
et al., 2019). The system we use is active, which al-
lows getting rid of illumination variations (e.g. shad-
ows) and therefore, to obtain infrastructure-intrinsic
Earlier methods (see e.g. (Bar Hillel et al., 2014)
for a survey) used an algorithmic approach in which,
typically, putative marking elements are first extracted
from the images by global thresholding (a great clas-
sic is the Otsu method), or by local thresholding and
geometric selection (Veit et al., 2008). Then, curves
are fitted to the detections in a robust manner, for ex-
ample using the Hough transform (Em et al., 2019) or
M-estimators (Tarel et al., 2002), to model marking
lines. Although these methods have made it possible
to drive vehicles automatically for a long time, they
involve adjusting many parameters and their detec-
tion performances have now been largely surpassed
by those of deep learning methods.
Deep learning approaches, based on convolutional
neural networks (CNNs), require the annotation of a
huge number of markings on images, which can be
very time consuming. We propose automatisms, im-
plemented in an ergonomic graphical user interface,
which provide a non-negligible help to the operator.
Once the CNN has been trained from the annotated
images, it can be used to detect the road markings and
then, to model the marking lines, see e.g. (Liang et al.,
2020; Zhang et al., 2022).
All the above methods are dedicated to the detec-
tion of road markings. Among the few publications
that deal with the evaluation of road markings, we can
cite (Lee and Cho, 2023), where a Mask R-CNN (He
et al., 2017) model is applied to detect the markings
in the image. Then, Otsu thresholding is used to seg-
ment it and a quality measure, that appears to be the
complement to one of the PRM, is computed. Using
the Otsu method for segmentation seems to us some-
what incomprehensible, since Mask R-CNN is an in-
stance segmentation algorithm and, as such, already
provides a segmentation. In (Soil
an et al., 2022), the
intensity of laser scanning data is processed to evalu-
ate the performance of road markings and to relate it
to retroreflection (the standard measurement of mark-
ing quality of use in night conditions). The process
involves a segmentation of the markings, but no de-
tails on this step are given in the paper. We note that
both approaches include a segmentation step, which
we do not consider necessary to estimate the quality
measure of the marking from its visual appearance.
IMPROVE 2024 - 4th International Conference on Image Processing and Vision Engineering
3.1 LCMS Sensor
The vehicle used for experiments, shown on fig. 2,
was developed to monitor road pavement degradation
on the French national network. It is equipped with a
pair of high resolution LCMS (Laser Crack Measure-
ment System) sensors developed by the Pavemetrics
company (Laurent et al., 2014). Each LCMS sensor is
made of a laser which emits a line to the road surface,
and of a linear camera that records the deformation
and the intensity of the reflected signal. The resulting
1D depth and intensity profiles are stacked to gener-
ate couples of 2D images. In our experiments, points
are acquired every 1 mm for each profile and profiles
are acquired every 5 mm. In this study, the focus is on
intensity, which corresponds to a retroreflected sig-
nal with both an emission and an observation angle of
, a geometry well suited to the estimation of PRM.
The signal is quantized into 256 gray levels, a spatial
equalization preprocessing is applied and the images
from the two sensors are merged to cover the entire
width of the lane (see example in fig. 5).
Figure 2: Picture of Aigle3D, with its 4 m measuring swath
depicted in green.
3.2 Experimental Sites and Datasets
Five experimental campaigns were carried out to ac-
quire the whole LCMS data. Quantitative and quali-
tative information on the recorded datasets is summa-
rized in table 1. Note that the first two experiments
(named Satory and Satory-2023) took place in a con-
fined test site. In order to simulate different levels
of marking wear in a controlled way, stencils were
used (see fig. 3), which provides a physical reference
for PRM. The other three acquisition campaigns took
place on open roads. We have selected different types
of roads with relatively varied marking conditions,
and such that the most usual road marking modules
are represented in the database.
Figure 3: Sample stencil (top) producing a road marking
with 45% PRM.
4.1 Annotation
The model to be trained aims at providing the PRM
and contrast for each detected marking. These metrics
must therefore be previously known for every mark-
ing of the training and testing datasets. As explained
in section 3.2, some stencils can be used to imple-
ment markings with known PRM. Such direct, physi-
cal PRM reference can be made available for a few
dozen examples on confined roads (e.g. in Satory,
we have PRMs of about 23%, 41% and 100%) but
this strategy cannot be implemented on a large-scale
on open roads. For this reason, we have developed
an approach that computes PRM and contrast indirect
reference measures using image processing methods
as follows:
1. The operator selects manually in the image an
area with a road marking (see fig. 4-A);
2. Image binarization (marking/non-marking) is per-
formed based on a gaussian mixture modeling
(GMM) of pixel intensities: the component with
highest mean is selected as marking. Mathemati-
cal Morphology tools are then applied either to re-
move small connected components (opening oper-
ation) or to fill in small holes in the region (closure
operation). The resulting black and white image
is shown in fig. 4-B;
3. The different connected components correspond-
ing to a same marking are manually merged to
provide a single connected component (see fig. 4-
4. The segmentation is divided into k rectangles (k
chosen by the user), as shown in fig. 4-D. For
each part, the rectangle minimizing the surface
area that contains the contour points is selected;
Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging
Table 1: Description of the LCMS datasets.
Name Road type Length (km) # images Comments
Satory Closed test site 1.7 71 Physical references (stencils)
Satory-2023 Closed test site 1.5 50 Physical references (more stencils)
Site 2 Major roads 14.0 619 Markings in fairly good condition
Site 3 Urban road 8.5 709 Markings in fairly good condition
Rouen area Urban & secondary roads 37.0 1203 Markings in varying conditions
5. The union of all rectangles defines the polygo-
nal envelope of the marking. The polygon is
smoothed to obtain a uniform marking width
along the entire length. The final envelope is rep-
resented in orange in fig. 4-E. The blue rectangu-
lar box, parallel to image axis, is the bounding box
used as ground truth for the training;
6. The PRM is computed as the ratio between the to-
tal number of pixels considered as road marking
within the orange envelope and the area of the en-
7. Considering the orange envelope area, a maximal
contrast ratio is also computed between the mark-
ing and the surrounding pavement:
Contrast =
RoadU p
, I
where I
is the mean intensity of the seg-
mented marking and I
RoadU p
(resp. I
) is
the average intensity of the pavement above (resp.
below) the marking, on a surface equal to that of
the envelope.
It should be observed that this approach requires
several operator-machine interactions. We use a mod-
ified version of the S3A software (Jessurun et al.,
2020) in which we have integrated our own func-
tionalities for helping the operator in these tasks (see
fig. 5). In the upper part of the interface the processed
image is displayed, surrounded by two panels for set-
ting the visualization and processing parameters. The
lower part displays the images at a larger scale (along
a route). The annotated marking lines are displayed
in color, with one color per marking typology, and the
image being processed highlighted in blue. Note that
image annotation and calculation of reference values
for PRM and contrast are performed on full resolution
4.2 Modified EfficientDet Architecture
We propose a modified version of EfficientDet (Tan
et al., 2020), a one-stage object detection network.
This architecture is at least as efficient as other con-
volutional networks dedicated to object detection, but
involves much less parameters. The modified archi-
tecture is shown in fig. 6. We use the backbone Ef-
ficientNetv2, proposed in (Tan and Le, 2021) for our
implementation. It is a bottom-up network that ex-
tracts features from the highest to the lowest spa-
tial resolution. In the classical Feature Pyramid Net-
work (FPN) (Lin et al., 2017a), feature maps are pro-
gressively upsampled as higher resolutions, in a top-
down pathway, where skip-connections are used to
directly re-introduce semantic information from the
backbone feature map at the same resolution. In Ef-
ficientDet, the authors propose a bi-directional Fea-
ture Pyramid Network (see biFPN layer in fig. 6), in
which multi-scale feature maps are merged in both a
top-down and a bottom-up flow so as to achieve a bet-
ter cross-scale feature network topology with limited
extra cost. In the original EfficientDet architecture,
prediction heads are composed of two sub-networks.
In the original EfficientDet architecture, the pre-
diction heads are composed of two sub-networks. The
classification network identifies the category of the
detected object and provides a confidence score. In
our case, this head is adapted to binary classification:
marking or non-marking. The bounding box network
provides the position and size of the rectangle delim-
iting the object of interest. In this contribution, we
propose to add a third sub-network to this architec-
ture, namely the indicator net (see fig. 6), that imple-
ments a regression model to assess both the PRM and
contrast values of the detected markings directly. This
provides a visual assessment of marking quality.
In our modified EfficientDet model, the overall
loss function to be minimized is defined as:
L = L
+ L
+ L
where L
, used to train the classification sub-
network, is a focal loss (Lin et al., 2017b), with pa-
rameters γ = 1.5 and α = 0.25. The regression mod-
els for bounding box and indicators nets are trained
by minimizing L
and L
, respectively. Both
are Huber losses with parameter δ = 0.1.
IMPROVE 2024 - 4th International Conference on Image Processing and Vision Engineering
Figure 4: Illustration of the annotation pipeline (the deformation of the marking is exaggerated by the non-uniform scaling).
Figure 5: Illustration of the annotation software (better visualized in the digital version).
Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging
Figure 6: Our modified version of EfficientDet. The additional sub-network (indicator net) appears in the green rectangle.
4.3 Training Protocol
We train our model by transfer learning from the
weights issued from EfficientNetv2, itself trained on
ImageNet-1k dataset (Tan and Le, 2021). As is cus-
tomary in this type of fine-tuning process, we opti-
mize the parameters of the components of the model
(backbone, biFPN and prediction heads) according to
a sequential protocol: first we specialize the model
for the application by freezing the backbone (hence
the feature extractor parameters) and training the rest
of the architecture, then we release all parameters and
train them, finally, we refine the model by repeating
the first step. More specifically, we tested three vari-
ants of this strategy, described in table 2, leading to
three models whose prediction performance will be
discussed in subsection 5.3.
In the first protocol (leading to model A), we first
train our architecture as a classical EfficientDet, i.e.
optimizing solely its detection components (classifi-
cation and bounding box nets), and only then do we
proceed with the separate training of the additional
head in charge of indicator estimation (indicator net).
In the third protocol (leading to model C), we opti-
mize the entire proposed architecture, end-to-end. Fi-
nally, the second protocol (leading to model B) is an
intermediate strategy, which starts like strategy A and
ends like strategy C.
Note that, in order to be consistent with the
3-component image format used in the pretrained
model (Tan and Le, 2021), we had to replicate two
times the single intensity component of LCMS im-
ages. Using the full resolution images of 4160×4000
would have required great time and computation re-
source, so we downsize the images by a factor of
about 6. Tests have shown that the impact of this sub-
sampling on the PRM is about 2.6%. Finally, in order
to accommodate the great number of images required
to train deep learning models, we use geometric data
augmentations during training. Morespecifically, im-
ages are randomly scaled with a factor between 0.75
and 1.5, rotated with an angle between 10° and 10°,
shifted by a displacement between 10% and 10%,
then cropped and zero padded into a 704 × 704 pixel
5.1 Reference PRM Evaluation
As mentioned in sec. 4.1, the PRM and contrast ref-
erence values used for training and testing the indi-
cator net are indirect, in that they are established by
image processing during the annotation. However, a
physical reference value is available for markings im-
planted with stencils of known PRM. We used them
to evaluate the indirect referencing process. For ex-
ample, on two populations of 48 annotated markings
with a theoretical (stencil) PRM of 23%, we obtained
24.52% and 24.50% mean PRM with standard devia-
tions of 2.06% and 3.45%, respectively. Likewise, on
2 × 48 annotated markings with a theoretical PRM of
41%, we obtained a mean PRM of 40.97% and 41.4%
with standard deviations of 4.95% and 3.55%. Finally
all new markings with a 100% theoretical PRM were
evaluated. The mean is 99.97% with standard devia-
tion of 0.101%. These experiences give a clear indica-
IMPROVE 2024 - 4th International Conference on Image Processing and Vision Engineering
Table 2: Definition of training strategies.
tion of the quality of our indirect referencing method-
5.2 Detection Evaluation
Models are trained from the 1893 images of the
datasets Satory, Site 2 and Rouen Area (see table 1).
We split the database in 80% images for training and
20% for validation. Due to the limited number of im-
ages available, a ve-fold cross-validation (CV) pro-
cedure is performed to train and evaluate the global
performance of algorithm.
We first evaluate the performance of models A,
B and C in terms of detection quality. To this aim,
we use all the images in dataset Site 3. We con-
sider a true positive detection if the overlap between
predicted and ground truth bounding boxes, defined
by the intersection-over-union (IoU) metric, is over a
given threshold Θ. For each value of Θ, a Precision-
Recall (P-R) curve can be plot by varying the detec-
tion threshold and computing the precision as the pro-
portion of true positive predictions among all predic-
tions and the recall, as the proportion of positives suc-
cessfully detected. The area under the P-R curve is the
average precision, AP
. Finally, the average of AP
computed for Θ {50, 55, 60, ..., 95}% is denoted by
AP (average precision).
Table 3: Detection results for the different models (see text).
Model AP
A 0.9890 0.9481 0.1456 0.8432
B 0.9905 0.9474 0.1236 0.8371
C 0.9800 0.8759 0.0508 0.7457
Figure 7: P-R curve for Model B (CV test subset #2). The
figures in table 3 correspond to the area under the
curves (displayed in colors).
Fig. 7 shows examples of P-R curves obtained
with model B, on one of the test subsets and table 3
summarizes the mean performance of the three evalu-
ated models over the five CV subsets (we observed
homogeneous performance over the subsets). One
may see that AP
decreases with Θ (and even falls for
Θ = 95%). A visual screening of the results showed
us that the bounding boxes provided by our models
have a slight tendency to be overestimated, which can
be partly explained by the way we handle the rotations
in the data augmentation step at at learning time. We
are planning modifications that should allow us to im-
prove this. Finally, we note that models A and B per-
form better than model C in detection, which is con-
sistent with the fact that the former are issued from an
optimization strategy favoring detection over param-
eter regression.
Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging
5.3 Quality Indicator Evaluation
We now evaluate the predictions of the PRM and con-
trast by our three learned models against the corre-
sponding indirect references. The provided statis-
tics, namely root-mean-squared differences (RMSD),
mean difference (Bias), standard deviations (STD),
median difference (Med), Median Absolute Deviation
(MAD), first and ninth decile of differences are aver-
ages resulting from cross-validation. We use as the
IoU threshold a value of Θ = 75%, which we have
found to provide a good compromise between the re-
quirement for detection accuracy and the quality of
the predicted PRM and contrast measurements. The
results are shown in table 4 for the PRM and in ta-
ble 5 for the contrast. As can be seen, model C is the
one that provides the best results for the PRM. For
the contrast, models B and C provide about the same
performance, better than model A. This experiment
confirms that it is more suitable to train the model di-
rectly, without trying to optimize the quality of the
detection first.
Table 4: Statistics of PRM differences between ground truth
and model predictions, with IoU = 75%.
Model RMSD Bias STD Med MAD Q10 Q90
A 10.92 -3.02 10.44 -3.34 6.15 -15.55 9.60
B 7.80 -0.10 7.78 -0.84 3.70 -8.41 9.08
C 6.70 -0.86 6.61 -1.30 3.12 -8.11 6.99
Table 5: Statistics of contrast differences with IoU = 75%
(contrast values lie between 1.5 and 8.9 in the test dataset).
Model RMSD Bias STD Med MAD Q10 Q90
A 1.21 -0.39 1.15 -0.06 0.45 -1.96 0.60
B 0.71 -0.27 0.66 -0.13 0.25 -1.03 0.32
C 0.72 -0.27 0.67 -0.13 0.25 -1.04 0.34
Table 6: Statistics of PRM differences between model pre-
dictions and physical reference.
A 17.23 2.11 17.22 0.06 13.15 -20.79 25.69
B 7.16 1.84 6.37 1.83 5.09 -6.05 10.24
C 6.16 2.12 5.41 2.09 3.93 -4.78 8.41
Finally, it is desirable to evaluate the quality of the
PRM regression against physical references. To this
end, we recently conducted a new acquisition cam-
paign on a stencil-marked site (Satory-2023, see Ta-
ble 1), using seven PRM reference values (approxi-
mately 25, 45, 56, 65, 74, 80 and 100%), but on a
limited number of samples (8 per PRM value). The
statistics in Table 6 show that model C gives the best
performance, and that the differences are quite small
(around 2%, with a dispersion around 5%). Careful
examination of the results, however, shows that the
bias is greater for low PRM values than for values
above 50%. This result is consistent with the statis-
tics of our training dataset, where worn-out markings
are underrepresented.
In this paper, we have proposed an innovative method
for assessing the quality of road markings, which
could facilitate their inspection. More specifically, we
have proposed a neural architecture that enables esti-
mating two indicators of road marking quality (Per-
centage of Remaining Marking and contrast), directly
from their visual appearance.
It is not possible to build, at least not on a large
scale, a physical reference for these measurements
and one must thus resort to an indirect reference com-
puted by image processing at annotation time. The
experimental results we report show the validity of
using an indirect reference.
Experimental results also show that it is possible
to correctly estimate road marking quality indicators
directly from their visual appearance, using a properly
trained neural network, without the need for prior seg-
mentation. Moreover, at training time, it is not neces-
sary to separately optimize the detection part of our
architecture: it can be trained directly end-to-end by
fine-tuning from a pre-trained model.
Lastly, preliminary results from new experimen-
tal measurements (with a limited number of samples,
however) suggest excellent agreement between pre-
dicted PRM and physical reference values. We be-
lieve that these results can be further enhanced by im-
proving some points of the training procedure, as well
as by increasing the corpus of data, paying particular
attention to poor quality markings.
This work received financial support of the ADEME
French project SAM (Safety and Acceptability of Au-
tonomous Mobility), funding number 1982C0034.
Bar Hillel, A., Lerner, R., Levi, D., and Raz, G. (2014).
Recent progress in road and lane detection: a survey.
Machine vision and applications, 25(3):727–745.
Carlson, P. J. and Poorsartep, M. (2017). Enhancing the
Roadway Physical Infrastructure for Advanced Vehi-
IMPROVE 2024 - 4th International Conference on Image Processing and Vision Engineering
cle Technologies: A Case Study in Pavement Mark-
ings for Machine Vision and a Road Map Toward
a Better Understanding. In Transportation Research
Board 96th Annual Meeting. Number: 17-06250.
CS 126 (2022). Inspection and assessment of road markings
and road studs. Dmrb, Highways England: Guild-
ford, UK; Transport Scotland: Edinburgh, UK; Lly-
wodraeth Cymru Welsh Government: Cardiff.
Em, P. P., Hossen, J., Fitrian, I., and Wong, E. K. (2019).
Vision-based lane departure warning framework. He-
liyon, 5(8):e02169.
He, K., Gkioxari, Doll
ar, P., and Girshick, R. a. (2017).
Mask R-CNN. In IEEE International Conference on
Computer Vision (ICCV), pages 2980–2988.
Jessurun, N., Paradis, O., Roberts, A., and Asadizanjani, N.
(2020). Component detection and evaluation frame-
work CDEF): A semantic annotation tool. Microscopy
and Microanalysis, 26(S2):1470–1474.
Laurent, J., H
ebert, J. F., Lefebvre, D., and Savard, Y.
(2014). 3D laser road profiling for the automated mea-
surement of road surface conditions and geometry. In
17th International Road Federation World Meeting,
volume 2, page 30.
Lee, S. and Cho, B. H. (2023). Evaluating Pavement
Lane Markings in Metropolitan Road Networks with
a Vehicle-Mounted Retroreflectometer and AI-Based
Image Processing Techniques. Remote Sensing,
Liang, D., Guo, Y.-C., Zhang, S.-K., Mu, T.-J., and Huang,
X. (2020). Lane Detection: A Survey with New Re-
sults. Journal of Computer Science and Technology,
Lin, T.-Y., Doll
ar, P., Girshick, R., He, K., Hariharan, B.,
and Belongie, S. (2017a). Feature pyramid networks
for object detection. In IEEE conference on computer
vision and pattern recognition, pages 2117–2125.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll
ar, P.
(2017b). Focal loss for dense object detection. In
IEEE international conference on computer vision,
pages 2980–2988.
Mesenberg, H.-H. (2003). Ztv m 02: Die neuen
atzlichen technischen vertragsbedingungen und
richtlinien f
ur markierungen auf straßen. German reg-
ulation, BAST.
NF EN 1824 (2020). Road marking materials - road trials.
French standard, CEN.
an, M., Gonz
alez-Aguilera, D., del Campo-S
A., Hern
opez, D., and Del Pozo, S. (2022).
Road marking degradation analysis using 3D point
cloud data acquired with a low-cost Mobile Mapping
System. Automation in Construction, 141:104446.
Tan, M. and Le, Q. (2021). EfficientNetv2: Smaller mod-
els and faster training. In International conference on
machine learning, pages 10096–10106. PMLR.
Tan, M., Pang, R., and Le, Q. V. (2020). EfficientDet:
Scalable and Efficient Object Detection. In 2020
IEEE/CVF Conference on Computer Vision and Pat-
tern Recognition (CVPR).
Tarel, J.-P., Ieng, S.-S., and Charbonnier, P. (2002). Us-
ing robust estimation algorithms for tracking explicit
curves. In 7th European Conference on Computer Vi-
sion (ECCV), pages 492–407.
Veit, T., Tarel, J.-P., Nicolle, P., and Charbonnier, P. (2008).
Evaluation of Road Marking Feature Extraction. In
11th International IEEE Conference on Intelligent
Transportation Systems, pages 174–181.
Zhang, D., Xu, X., Lin, H., Gui, R., Cao, M., and He, L.
(2019). Automatic road-marking detection and mea-
surement from laser-scanning 3D profile data. Au-
tomation in Construction, 108:102957.
Zhang, Y., Lu, Z., Zhang, X., Xue, J.-H., and Liao, Q.
(2022). Deep Learning in Lane Marking Detection: A
Survey. IEEE Transactions on Intelligent Transporta-
tion Systems, 23(7):5976–5992.
Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging