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Texture Analysis Based Damage Detection of Ageing Infrastructural Elements
Author(s) -
O’Byrne Michael,
Schoefs Franck,
Ghosh Bidisha,
Pakrashi Vikram
Publication year - 2013
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2012.00790.x
Subject(s) - pixel , computer science , artificial intelligence , feature vector , chromatic scale , feature (linguistics) , range (aeronautics) , segmentation , pattern recognition (psychology) , computer vision , support vector machine , color space , visual inspection , texture (cosmology) , data mining , mathematics , image (mathematics) , engineering , linguistics , philosophy , combinatorics , aerospace engineering
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.