PRIMITIVE-BASED CLASSIFICATION OF PAVEMENT CRACKING IMAGES

Collection and analysis of pavement distress data are receiving attention for their potential to improve the quality of information on pavement condition. We present an approach for the automated classification of asphalt pavement distresses recorded on video or photographic film. Based on a model that describes the statistical properties of pavement images, we develop algorithms for image enhancement, segmentation, and distress classification. Image enhancement is based on subtraction of an ''average'' background: segmentation assigns one of four possible values to pixels based on their likelihood of belonging to the object. The classification approach proceeds in two steps: in the first step, the presence of primitives (building blocks of the various distresses) is identified, and in the second step, classification of images to a distress type (using the results from the first step) takes place. The system addresses the following distress types: longitudinal, transverse, block, alligator cracking, and plain. Application of the models to a set of asphalt pavement images gave promising results.