Dynamic Programming and Connected Component Analysis for an Enhanced Pavement Distress Segmentation Algorithm

Abstract

Automatic pavement distress segmentation is essential for automatic classification and evaluation of pavement conditions during maintenance. Improving the speed and accuracy of the many algorithms for image-based pavement crack segmentation remains a challenge. Although a dynamic programming–based (DP-based) algorithm is more accurate than other crack segmentation methods, its practical use is limited by the required long computation time. A fast algorithm for pavement crack segmentation needs to be developed with the use of DP and multiscale characterization of fundamental crack elements on connected components in grid cells. The proposed algorithm integrated the accuracy of DP and the high speed of grid cell and connected component analyses. Region-based nonuniform background illumination was removed, and the pre-processed image was divided into grid cells. Multiscale characterization of fundamental crack elements was conducted to differentiate the significant crack elements from the noncrack components on the basis of the connected component in the cells on multiple scales. The crack regions of interest were then accurately estimated through the most significant crack elements and fed into the DP-based crack segmentation with the probabilistic scoring function. The proposed algorithm was tested on a diverse set of pavement images, provided by the Georgia Department of Transportation, taken from I-75/85 under varying lighting conditions near Atlanta. A buffered Hausdorff measure was used to quantitatively evaluate the accuracy of the proposed crack segmentation algorithm. Experimental results showed that the proposed algorithm ran three times faster than the original DP-based method while providing the same accuracy. The proposed algorithm is promising for the practical generation of pavement crack maps.