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Proceedings Paper • Open Access

Automatic crack detection on concrete floor images
Author(s): Christophe Simler; Erik Trostmann; Dirk Berndt

Paper Abstract

This paper presents an algorithm detecting automatically a wide variety of cracks on monochrome images of concrete floors. It is part of a vision system, generating a crack map to support the condition monitoring for buildings. The suggested method uses successively radiometric, geometric and contextual information. An automatic supervised adaptive intensity-threshold method handles radiometry information. A threshold-based method was chosen because it can separate even thin cracks from the background. In order to cope with different image cluster intensities, first a clustering algorithm separates regions of different intensities, and second the threshold is adaptive at the cluster level; being a continuous function of the cluster intensity. At some points, function values were learned via supervised classification. Then, we performed an interpolation between these points in order to get a threshold whatever the cluster intensity (continuity). At this step, we have a thresholded image. However, due to texture, non-crack dark defects and dirtiness we have false positives. In order to overcome this problem, connected pixels are grouped into regions, and after discarding small regions, a size-dependent geometrical shape filter is suggested. The shape of a crack region depends on its area, and this relation was empirically learned. A region is retained if its shape features are above some area depending thresholds. However, mainly due to texture, we have still false positives because some non-crack entities have radiometry and geometry of cracks. Fortunately, they are often small isolated regions and are discarded via an isolation filter. Tests performed on many images show very encouraging results.

Paper Details

Date Published: 17 September 2019
PDF: 10 pages
Proc. SPIE 11144, Photonics and Education in Measurement Science 2019, 111440T (17 September 2019);
Show Author Affiliations
Christophe Simler, Fraunhofer Institute for Factory Operation and Automation (Germany)
Erik Trostmann, Fraunhofer Institute for Factory Operation and Automation (Germany)
Dirk Berndt, Fraunhofer Institute for Factory Operation and Automation (Germany)


Published in SPIE Proceedings Vol. 11144:
Photonics and Education in Measurement Science 2019
Maik Rosenberger; Paul-Gerald Dittrich; Bernhard Zagar, Editor(s)

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