Texture Analysis based Detection and Classification of Surface Features on Ageing Infrastructure Elements

Michael O'Byrne (1) , Bidisha Ghosh (1) , Vikram Pakrashi (2) , Franck Schoefs (3)

1 Trinity College Dublin
2 UCC - University College Cork
3 GeM - Institut de Recherche en Génie Civil et Mécanique

This paper presents a texture analysis based approach for the detection of damaged regions on the surface of infrastructural elements. A k-means clustering algorithm was used to partition regions with similar textural properties. Four texture measures were derived from a Grey Level Cooccurrence Matrix (GLCM) namely; contrast, homogeneity, entropy and Angular Second Moment (ASM). The approach is validated successfully on an image of a damaged concrete bridge beam. The performance of this Non-Destructive Testing (NDT) technique is evaluated for various values of k through the use of performance points in the Receiver Operating Characteristic (ROC) space. The technique may be deployed as a Structural Health Monitoring (SHM) tool to track the extent of surface damage, and can used in a Bridge Management System (BMS) to aid structured decision making and scheduling of repair work.

Mots clés: Damage Detection; Image Processing; Texture Analysis; Grey Level Co-occurrence Matrix; k-means Clustering; Bridge Management Systems

Domaines: Mécanique [physics.med-ph]; Matériaux

Michael O'Byrne, Bidisha Ghosh, Vikram Pakrashi, Franck Schoefs. Texture Analysis based Detection and Classification of Surface Features on Ageing Infrastructure Elements. BCRI2012 Bridge & Concrete Research in Ireland, 2012, Cork, Ireland. ⟨hal-01009012⟩