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

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. 1. Introduction Engineering components made from structural steel 304 metals are being used in many industries, commercial and domestic fields, because of their chemical durability, mechanical properties, weldability, good corrosion, and heat resistant properties. Corrosion of stainless steel in aggressive environmental conditions in particular is a fundamental concern to academia and industry due to the destructive nature of corrosion on mechanical properties of components. Ships, storage tanks, bridges, and pipelines commonly made from structural steel are not sufficiently resistant to corrosion in their operating environments which impacts corrosion costs associated with maintenance and also safety risks. Therefore, corrosion monitoring is an important issue in detecting corrosion damage and its growth before failures occur [1–3]. There are many different experimental and analysis methods used for corrosion inspection and monitoring purposes. These include mechanical measurements (weight loss), chemical analysis, and visual inspections. In visual inspections, the corrosion damage identification requires an expert who can clearly demarcate the corrosion based on experience as well as types of corrosion, with red rust as a common experience. Usually, the corrosion process produces rough surfaces, and image analysis based on textural features can be used for quantification and discriminate corrosion extent and type. Several authors investigated automatic corrosion detection using image processing techniques [4–7]. Computer image processing involves definition and development of techniques and algorithms for processing pictorial data