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A Method for Crack Detection on a Concrete Structure

Authors: Yusuke Fujita; Yoshihiro Mitani; Yoshihiko Hamamoto;

A Method for Crack Detection on a Concrete Structure

Abstract

Recently, interest in automatic crack detection on concrete structure images for non-destructive inspection has been increasing. In general, there are various noises such as irregularly illuminated conditions, shading, blemishes and divots in the concrete images. These lead to difficulties for automatic crack detection. This paper presents two p re-processings in order to remove such noises for crack detection. First, slight variations like irregularly illuminated conditions and shading are removed from concrete images by the subtraction pre-processing with the smoothed image. Secondly, a line filter based on the Hessian matrix is used to emphasize line structures associated with cracks. Finally, thresholding processing is used to separate cracks from background. The performance of the proposed method is evaluated by ROC analysis with 50 real images. The experimental results show that the proposed method is effective for detecting cracks on noisy concrete images

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
121
Top 1%
Top 1%
Average
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