Smart Color Image Recognition for Steel Bridge Rust Inspection
Date Issued
2009
Date
2009
Author(s)
Yang, Ya-Ching
Abstract
Image processing has been widely utilized in scientific research and prevalently adopted in industries. Application in infrastructure condition assessment includes defect recognition on steel bridge painting and underground sewer systems. Nevertheless, there is still no robust method to overcome the non-uniform illumination problem. Although, the K-Means is recognized as one of the best rust defect recognition methods, it cannot recognize the non-uniform illuminated images and the mild rust color well. Also, there is lack of an automated color image recognition system in this field.his research starts with an investigation of 14 color spaces in order to find out a comparatively proper color configuration for non-uniformly illuminated rust image segmentation. Among the 14 color spaces, the color configuration of a*b*, which has moderate ability to filter light, is utilized to develop the proposed two models, adaptive ellipse approach (AEA) and box and ellipse-based neural fuzzy approach (BENFA).n the adaptive ellipse approach (AEA), a rust image is partitioned into three parts, background, rust, and the gradual change color from mild-rust to background. The main idea is to deal with the gradual color change properly for mild rust color extraction. The background colors can be automatically detected from a rust image. A fundamental ellipse is previously defined by the collection of rust colors. The AEA enlarges the fundamental ellipse to include part of the gradual change in color, and the enlarged size depends on the relationship between the rust color and the color of coating. The AEA is expected to deal with the boundary between background color and rust color properly. In addition, illumination adjustment is adopted in this model in order to overcome the non-uniform illumination problem. Finally, the processing results of the AEA are compared with the K-Means clusters method to show that it can recognize the mild-rust-colors.hen the color distribution is almost parallel to the major axis of the fundamental ellipse, the proposed AEA may not recognize the mild-rust-colors well. Therefore, the box and ellipse-based neural fuzzy approach (BENFA) is proposed to deal with the gradual color change from mild-rust to background. The BENFA applies the adaptive-network-based fuzzy inference system (ANFIS) to describe the gradual change colors. In order to achieve automated detection, the BENFA applies the automated detection of background, illumination adjustment, and the fundamental ellipse to determine the thresholds of serious rust and mild rust. Compared to the Fuzzy C-Means (FCM), the BENFA can stably recognize the rust intensity.he third model which is called BEMD-morphology approach (BMA) aims to adjust the color of a non-uniformly illuminated rust image. The BMA applies the bidimesional empirical mode decomposition (BEMD) to mitigate the shade/shadow effect, and morphology to substitute the highlight points by the neighboring colors. Processing a rust image with the BMA is more reliable than processing without the BMA.inally, conclusions will be drawn and recommendations for future work will be made.
Subjects
Coating defect recognition
image processing
K-Means
adaptive-network-based fuzzy inference system (ANFIS)
Fuzzy C-Means
bidimensional empirical mode decomposition (BEMD)
morphology
Type
thesis
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