Abstract:
With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing
need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to
the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data
processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road
pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated
pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and
extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary
pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-
invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect
to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are
based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an
average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP
respectively.
