Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

Abstract

Corrosion surface damage in the form of pitting and microcracks is observed in many systems and affects the integrity of steel structures in nuclear, civil, and industrial engineering. In order to gain a better understanding and develop nondestructive and automatic detection/assessment of corrosion damage and its growth, an image analysis based on texture using wavelet transforms and color features was carried out. Experiments were conducted on steel 304 panels under three different electrolyte solutions, and periodic scans were used to obtain the images for analysis over time. The results obtained from the image analysis are presented to illustrate the metrics which best characterize early stage corrosion damage growth behavior. The results obtained indicate that textural features in combination with color features are more effective and may be used for correlating service/failure conditions based on corrosion morphology.

Application of Histogram Equalization for Image Enhancement in Corrosion Areas

“…[1], light source, distance, and angle [2]. And, therefore, the quality of the corrosion image will be limited to a certain extent especially the corrosion picture inside the metal [3]. For subsequent detection and recognition of corrosion morphology, it is important to enhance this image [4].…”

An In-Silico Corrosion Model for Biomedical Applications for Coupling With In-Vitro Biocompatibility Tests for Estimation of Long-Term Effects

“…In order to characterize corrosion damage growth qualitatively and quantitatively, image analysis had to be performed. Some papers discussed the concept of creating textural/color features that are resilient to corrosion images, using a low-tech method that uses a commercial color scanner ( Pidaparti, Hinderliter and Maskey 2013 ; Medeiros, et al, 2010 ). An investigation of the textural characteristics of wavelet transformations and color features was performed to define the corrosion damage metrics during corrosion growth under three different electrolyte solutions ( Pidaparti et al, 2013 ).…”

“…Some papers discussed the concept of creating textural/color features that are resilient to corrosion images, using a low-tech method that uses a commercial color scanner ( Pidaparti, Hinderliter and Maskey 2013 ; Medeiros, et al, 2010 ). An investigation of the textural characteristics of wavelet transformations and color features was performed to define the corrosion damage metrics during corrosion growth under three different electrolyte solutions ( Pidaparti et al, 2013 ). In order to explain corrosion development in time ( Pidaparti et al, 2013 ), the strategy by Pidaparti et al was to combine functionality mitigation properties due to material compromise (textural features) with local aspects (color values).…”

“…An investigation of the textural characteristics of wavelet transformations and color features was performed to define the corrosion damage metrics during corrosion growth under three different electrolyte solutions ( Pidaparti et al, 2013 ). In order to explain corrosion development in time ( Pidaparti et al, 2013 ), the strategy by Pidaparti et al was to combine functionality mitigation properties due to material compromise (textural features) with local aspects (color values). Kapsalas et al ( Kapsalas et al, 2007 ) suggested a system for detecting corrosion size and topology in stonework surfaces by checking and analyzing image segmentation schemes.…”