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Texture Analysis Based Damage Detection of Ageing Infrastructural Elements
Date Issued
2012-11-08
Date Available
2019-05-14T08:11:08Z
Abstract
To make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions.
Sponsorship
Irish Research Council for Science, Engineering and Technology
Other Sponsorship
CAPACITES/IXEAD Society
Type of Material
Journal Article
Publisher
Wiley Online Library
Journal
Computer-Aided Civil and Infrastructure Engineering
Volume
28
Issue
3
Start Page
162
End Page
177
Copyright (Published Version)
2012 Computer-Aided Civil and Infrastructure Engineering
Language
English
Status of Item
Peer reviewed
ISSN
1093-9687
This item is made available under a Creative Commons License
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