Pavement Crack Classification via Spatial Distribution Features. (20th March 2011)

• Record Type: Journal Article
• Authors: Li Li, Qingquan Qingquan; Zou Zou, Qin Qin; Liu Liu, Xianglong Xianglong
• Other Names: Liao Liao Mark Mark Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2011(2011)
• Publication Date: 2011-03-20
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2011/649675
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : Pavement crack types provide important information for making pavement maintenance strategies. This paper proposes an automatic pavement crack classification approach, exploiting the spatial distribution features (i.e., direction feature and density feature) of the cracks under a neural network model. In this approach, a direction coding (D-Coding) algorithm is presented to encode the crack subsections and extract the direction features, and a Delaunay Triangulation technique is employed to analyze the crack region structure and extract the density features. As regarding skeletonized crack sections rather than crack pixels, the spatial distribution features hold considerable feature significance for each type of cracks. Empirical study indicates a classification precision of over 98% of the proposed approach.