CrackTree: Automatic crack detection from pavement images

  • Authors:

  • Qin Zou;Yu Cao;Qingquan Li;Qingzhou Mao;Song Wang

  • Affiliations:

  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, PR China and Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry ...;Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA;Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan 430079, PR China and State Key Laboratory of Information Engineering i ...;Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan 430079, PR China and State Key Laboratory of Information Engineering i ...;Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.