Machine Vision-Based Concrete Surface Quality Assessment
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 2
Abstract
Manually inspecting concrete surface defects (e.g., cracks and air pockets) is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects (i.e., air pockets and discoloration). These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects’ visual impact ratios (VIRs). The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance.
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Acknowledgments
Ms. Gauri Jog prepared the survey form for manually inspecting concrete surface images; Mr. Stephen Kemp kindly performed manual inspection for this paper; and Mr. Ryan Tubbs proofread the paper. All above supports are gratefully acknowledged here.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 2 • February 2010
Pages: 210 - 218
Copyright
© 2010 ASCE.
History
Received: Jun 25, 2008
Accepted: Jul 19, 2009
Published online: Aug 1, 2009
Published in print: Feb 2010
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