Matched Filtering Algorithm for Pavement Cracking Detection

Abstract

There have been rapid developments in automated surveying of cracking pavements in recent years. Laser-imaging technology has made the acquisition of shadow-free images feasible. However, because of the complexity of pavement surfaces, the diverse characteristics of cracks, the presence of foreign objects, and varying identification protocols, the results of automated cracking recognition have had limited use. A matched filtering algorithm is introduced for detection of pavement cracking. Unlike traditional edge detection approaches that adopt first-or second-order derivatives of image signals, the matched filtering algorithm detects cracks by matching predesigned filters with crack features by shape, orientation, or intensity. Experiments were conducted to compare the results of five traditional edge detectors (Roberts, Prewitt, Sobel, Laplacian of Gaussian, and Canny) and the matched filtering algorithm. The matched filtering algorithm was shown to be a robust approach for detecting cracks and had better performance in noise removal and detection of cracking. With matched filters aligned at various orientations, the matched filtering algorithm showed its distinctive advantages for extracting a single crack in its actual form and recording the crack's orientation to be used for more accurate classification in the next step of automated processing.