The need for objective collection of crack information has increased in
The type of wear of the road surfaces has changed. New, more stronger and
lasting pavements have been developed. The use of lightweight studs has also
changed the degree of wear. The use of thin pavements increases and the
life expectation has grown. The amount of heavy traffic increases and maximum
allowed axle load, as well as maximum allowed tire pressure increases. All
leads to a change from the traditional measures like rut depth to more
measures like crack information. In USA, Japan and south of Europe there is a
tradition in inventory of cracks. That is the reason why so many of the
crack measuring systems has their origin here. The first automatic systems
actually half automatic, they collected pictures of the surface and in a post
the pictures was subjectively judged to describe cracks. The judgment to
consists of two phases, severity level and degree of seriousness. This
inspection is a time consuming process and has a low level of repeatability.
first semiautomatic systems used film that had to be developed. This is still
but the technique to acquire films has improved and continues to improve.
are mainly three types of techniques that are used to day, namely:
1. Traditional video (analogue and digital)
2. Line scan video (analogue and digital)
3. Distance measuring laser cameras (point or line scan)
As examples of the three different techniques the following systems can be
mentioned: 1: PAVUE/Laser RST and WiseCrax (ARAN), 2: HARRIS, 3: G.I.E.
Laser Vision and Laser RST.
To get a complete automatic crack-measuring system there is also a need for
automatic classification and judgment. This is still the most difficult part
achieve. A lot of processor power and a well functioning classification model
needed to do this. To reduce the amount of data it would be an advantage to
this in real time.
Linköping: Statens väg- och transportforskningsinstitut., VTI notat 24-2002 , 2002.
Swedish, Sweden, Cracking, Flexible pavement, Measurement, Method, Detection, Automatic, Laser, Video camera, Image processing, Classification