Novel System for Automatic Pavement Distress Detection

Abstract

Statistics published by the Federal Highway Administration indicate that maintenance and rehabilitation of highway pavements in the United States requires an expenditure of over $17 billion a year. In conventional visual and manual pavement distress analysis approaches inspectors traverse roads and stop and measure distress objects when they are found. Therefore, the conventional approaches are very costly, time consuming, dangerous, labor intensive, tedious, subjective, have a high degree of variability, are unable to provide meaningful quantitative information, and almost always lead to inconsistencies in distress detail over space and across evaluations. In this paper, a new pavement distress image-enhancement algorithm and a new analysis and classification algorithm are studied. The enhancement algorithm corrects nonuniform background illumination by calculating multiplication factors that eliminate the background lighting variations. The new pavement distress classification algorithm builds a data structure storing the geometry of the skeleton obtained from the thresholded image. This data structure is pruned, simplified, and aligned, yielding a set of features for distress classification; the number of distress objects, number of branch intersections, number of loops, relative sizes of branches in each direction, etc. The experimental results demonstrate that the proposed algorithm can precisely quantify geometrical and topological parameters, quickly accept new classification rules for classification, and accurately estimate the distress severity from the thresholded image.