- Research Article
- Open Access
Pavement Crack Classification via Spatial Distribution Features
EURASIP Journal on Advances in Signal Processing volume 2011, Article number: 649675 (2011)
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.
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Li, Q., Zou, Q. & Liu, X. Pavement Crack Classification via Spatial Distribution Features. EURASIP J. Adv. Signal Process. 2011, 649675 (2011). https://doi.org/10.1155/2011/649675
- Neural Network Model
- Region Structure
- Delaunay Triangulation
- Full Article
- Classification Approach