Zhu, Z and Brilakis, I (2010) Machine vision based concrete surface quality assessment. Journal of Construction Engineering and Management, 136. pp. 210-218. ISSN 0733-9364Full text not available from this repository.
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. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0000126?journalCode=jcemd4
|Uncontrolled Keywords:||Defects Identifications Assessment Concrete Images Imaging techniques Information technology|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:26|
|Last Modified:||30 Nov 2015 17:21|