The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008
Considering different pavement types the IRI-diagrams can be
compared (Figure 8).
The standardized IRI values for airport runways and
superhighways are 0.5-2.0, 1.5-3.5 for new pavement, 2.5-5.5
for older pavement, 4.0-11.0 for damaged pavement and 8.0-
20.0 m/km for rough unpaved roads. Comparing the IRI values
of Figure 7, relative good fitting can be noticed: the renewed
pavement on the bridge has an average IRI value of 4 m/km,
whilst the older road segment on the east river side has about 16
m/km. Especially this latter value points out that our system
requires a calibration run to get the mathematical formula to
assign the IRI observations to the standardized evaluation.
Figure 8 illustrates the IRI diagrams for the investigated
pavement types, calculated from the raw vertical acceleration
measurements.
The first three pavement types are the smooth surfaces; they
have an average IRI value of about 0.1 with a relatively small
(< 0.05) standard deviation. Compared to this, the last two
pavements are worse as the observed higher IRI values (0.3 and
0.6) with larger standard deviation (> 0.05) clearly indicate it.
Although there are numerically greater differences between our
mean IRI values and the standardized ones, the tendency is
obvious: the worse road gets higher IRI measures. This solution
also cannot avoid the calibration procedure.
Figure 9. IRI road status in Budapest derived by the last measurement campaign
The most important statistics of the IRI-values are the following:
Pavement type
Mean
Std
deviation
Stone mosaic 1
0.1041
0.0195
Stone mosaic 2
0.1095
0.0164
Stone mosaic 3
0.1416
0.0317
Small cubes
0.6795
0.1586
Large cubes
0.3543
0.0775
Table 2. Basic statistics of IRI on different pavement types
(derived from Figure 8 diagrams)
7. CONCLUSIONS
In this paper we presented a concept study and our initial results
of the developed pavement detection system. Digital cameras
capture the images of the road pavement which is lit by
structured light. The exterior orientation parameters of the
images are provided by a GPS/INS navigation system, which
enables the pavement surface generation at good accuracy.
Applying a special diffuse illumination, the small anomalies of
the pavement, such as scars and potholes, can also be detected.
In the context of this effort we developed a road profile and
surface generation module which input the profiles formed from
the marker points. This CAD-based module not only computes
the profiles from the given points, but generates a road surface
model that represents the pavement condition and can be used
for maintenance assessment, scheduling, and planning.
The investigations and tests described in this paper proved that
the proposed single-camera detection method assures robust
solution for road surface generation. Applying a single imaging
unit simplifies the georeferencing and avoids the complicated
calculation and calibration of two cameras which have to be
synchronized. Using line projection instead of laser point array
enables measuring all point heights along the profile, therefore
the surface model resolution depends only on the horizontal
resolution of the camera. This concept also has further potential
in development, e.g. applying multiple projected lines in order
to allow higher measurement speed, and also leaves open the
possibility of using the camera images for additional purposes,
such as crack detection, which is a critical issue regarding road
condition detection.
The described mobile mapping system and the provided surface
data can be used for deriving the IRI for the particular road
segments, which is used by the transportation authorities for
road pavement classification.
The next step in development is creating the engineering
prototype of the road detection system; the camera, the laser
projector, the control unit, and the navigation system are to be
