PHOTOGRAMMETRIC PAVEMENT DETECTION SYSTEM
I. Kertész*, T. Lovas, A. Barsi
Budapest University of Technology and Economics, Budapest, Hungary - (ikertesz, tlovas)@mail.bme.hu,
barsi@eik.bme.hu
ICWG V/I
KEY WORDS: Road inspection, Pavement detection, Mobile mapping
ABSTRACT:
This paper introduces a complex, low-cost road pavement measurement system designed primarily for pothole and crack detection.
The onboard system is composed of a GPS/INS navigation unit, an image acquisition module, and a photogrammetric and image-
processing subsystem. Due to the use of structured light and the availability of accurate navigation data, the 3D coordinates of the
road points, in the form of cross profiles, as well as the size and location of the potholes, can be derived from the images at cm-level
relative accuracy. For the road classification the International Roughness Index (IRI) can be derived. This paper also discusses the
potential future developments of the system; it reports on the initial investigations of applying single camera with laser line
projection which can significantly simplify the overall procedure.
1. INRODUCTION AND CONCEPT
In the well-motorized countries the transportation authorities
spend significant amounts of money to road constructions and
road pavement maintenance. The roads are aging; the
maintenance works have to keep pace with the new
constructions. The traffic delays are mostly caused by road
closures, this topic is always highlighted in the news. Therefore,
the optimized scheduling became extremely important, the
authorities tend to turn to new technologies in order to map the
actual condition of the road pavement and decide upon the
priorities of the scheduled maintenance works.
Since road engineering is one of the most extended engineering
field, many “traditional” road surveying methods used for
construction and maintenance planning. As most of the
maintenance works are scheduled based on experiences, the
road conditions are often classified by visual observations. The
number of potholes, the length and type of the cracks, and lane
grooves are clear indicators of the overall road condition.
However, there are a few autonomous (or at least semi-
autonomous) systems in use, e.g. the Swedish RST
(http://www.opq.se) or the Mandli Roadview
(http://www.mandli.com): both systems are able to measure the
cross- and longitudinal profiles of the roads; moreover, Mandli
is collecting digital images about the road surface.
This paper discusses the structure and results of the original
system, the potential of the entry-level IMUs (inertial
measurement unit) in mobile mapping and introduces the latest
concept with the initial test results.
2. SYSTEM COMPONENTS
Our basic idea was to create a road pavement detection system,
which uses optical sensors for measuring the longitudinal and
cross sections of the roads and the pavement anomalies
(potholes, wide cracks). Figure 1 shows how the system
components are linked to each other. The sensor component
consists of two cameras (Sony XCD-SX910) synchronized by
external trigger and laser projectors, the location component
contains an integrated GPS/INS (Global Positioning System /
Inertial Navigation System) unit (Crossbow NAV420CA).
The cameras are capturing downward images about the
pavement at a rate of 5 fps. 20 individual laser projectors
provide the equally spaced marker points located perpendicular
to the travelling direction on the surface. Therefore, the 3D
coordinates of the points can be computed by evaluating
equations of geometric constraints.
The georeferenced position and orientation of the camera pair
are provided by the integrated GPS/INS system. The
NAV420CA system consists of a navigation grade GPS receiver,
an entry-level IMU (Inertial Measurement Unit), and a built-in
Kalman-filter. The accuracy of the positioning by the GPS
module is about 3 m CEP, whereas the random walk of the IMU
is about 4.5°/hrl/2 (Barsi et al. 2005).
The cameras have 1280 by 960 pixel resolution which results in
3 mm ground pixel size, which enable not only the detailed
surface description but the scar and crack detection from the
images, too. The camera location assures the full lane-width
visibility, covering 3.5 m wide area. The cameras are linked by
FireWire to the laptop mounted in the cab (Figure 2).
Corresponding author.
