A CLASSIFICATION ALGORITHM FOR TARGET AND NON-TARGET IMAGE FEATURE AREAS FOR SUPPROTING CONCRETE SURFACE INSPECTION

Abstract

 For better concrete surface inspection based on image analysis and interpretation, this paper has proposed a Target and non-target image Feature area Classification algorithm (termed “TFC algorithm”). Due to the difficulties of selecting training data sets of various texture features (e.g., fine crack, grain, roughness, stain, etc.) using ultra-high-resolution data in supervised classification, non-hierarchical unsupervised classification procedure is introduced to produce TFC and n-TFC images. The experimental results are summarized as follows: i) By using TFC image together with n-TFC image, candidate areas for inspection can be classified and ranked according to the strength of electromagnetic wave reflection, ii) especially, the surface floating area along borders of fine cracks, that is multiple scattering area, can be extracted and; iii) the prototype TFC system with semi-real-time processing function as well as superior operability has also been developed, which is useful as an idea creation support- and existing system combination-type system.