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.