期刊
PATTERN RECOGNITION LETTERS
卷 33, 期 3, 页码 227-238出版社
ELSEVIER
DOI: 10.1016/j.patrec.2011.11.004
关键词
Crack detection; Edge detection; Edge grouping; Tensor voting; Shadow removal
资金
- National Natural Science Foundation of China (NSFC) [40721001]
- Doctoral Foundation Program [20070486001]
- Fundamental Research Funds for the Central Universities [20102130101000130, 6082031]
- US National Science Foundation [NSF-1017199, NSF-0951754]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1017199] Funding Source: National Science Foundation
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. (C) 2011 Elsevier B.V. All rights reserved.
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