01025622

Component

Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. Previously developed image recognition methods for painting rust defect assessment can be summarized in two categories: the Neuro-Fuzzy Recognition Approach (NFRA) method and the Simplified K-Means Algorithm (SKMA) method. The NFRA method uses artificial intelligence techniques to separate rust pixels from background pixels. The SKMA method segments object pixels and background pixels in a digitized image using a statistical method, called the K-means algorithm. Even if both methods pass through different processing procedures, one common thing is that they first convert original color images to grayscale images and further process the grayscale images. This paper presents the application of previously developed image processing methods for painting rust defect evaluations and discusses their limitations under several specific environmental conditions which are often encountered while acquiring coating digital images.

01020180

06-1372

English

21p

2006

Transportation Research Board 85th Annual Meeting

CD-ROM

Figures (12) ; References (3)

Bridge management systems; Coatings, fillers and paints; Fuzzy algorithms; Image processing; Rust prevention; Statistical analysis

Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; I60: Maintenance

Transportation Research Board Annual Meeting 2006 Paper #06-1372

BTRIS, TRIS, TRB

Mar 3 2006 10:40AM