Image-based “D”-crack detection in pavements

Abstract

This thesis proposes an automated crack detection and classification algorithm to detect durability cracking (“D”-cracking) in pavement by using image processing and pattern recognition techniques. For the Departments of Transportation across the country, efficient and effective crack detection is vital to maintaining quality roadways. Manual inspection of roadways is tedious and cumbersome. Previous research has focus on distinct transverse and longitudinal cracks. However, “D”-cracking presents a unique challenge since the cracks are fine and have a distinctive shape surrounding the intersection of the transverse and longitudinal joints. This thesis presents an automated crack detection and classification system using several known image processing techniques. The algorithm consists of four sections: 1) lighting correction, 2) subimage processing, 3) postprocessing and 4) classification. Some images contain uneven lighting, which are corrected based on a model of the lighting system. The region of interest is identified by locating the lateral joints. These regions are then divided into overlapping subimages, which are then divided into cracked and noncracked pixels using thresholds on the residual error. Postprocessing includes a row/column sum filter and morphological open operation to reduce noise. Finally, metrics are calculated from the final crack map to classify each section as cracked or noncracked using the Mahalanobis distance from the noncracked distribution.