Measurement pitting morphology characteristic of corroded steel surface and fractal reconstruction model

Songbo, Ren; Kong, Chao; Gu, Ying; Gu, Shitan; Shenghui, Zeng; Li, Gang; Tao, Yang; Zhao, Yong

doi:10.1016/j.measurement.2021.110678

Cited by 13 publications

(1 citation statement)

References 27 publications

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“…Due to the advantages of being non-destructive and having high efficiency, high precision and low cost, the corrosion image recognition has received much attention and has achieved remarkable results in the corrosion morphology analysis and corrosion degree evaluation. The main image processing method includes Markov random fields [5,6], autoregressive models [7,8], fractal models [9][10][11], gray-level co-occurrence matrices (GLCM) [12][13][14], discrete wavelet transforms (DWTs) [15,16] and binary image processing [17,18]. Fajardo et al [12] identified the damage degree caused by different corrosion types through the gray-level co-occurrence matrix, which could extract useful characteristics from the corrosion morphology images.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Evaluation of Marine Corrosion of Q420 Steel Based on Image Recognition Method

Wang

Li²,

Jiang

et al. 2022

Coatings

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Marine engineering materials are prone to serious corrosion damage, which affects the efficiency and reliability of marine equipment. The diversity of corrosion morphology makes it difficult to achieve the quantification and standardization of the microscopic local information on the corroded surface, which is of great significance to reveal the multi-scale corrosion mechanism. In this paper, an image intelligent recognition method for the corrosion damage of Q420 steel in seawater is established, which is based on the gray level co-occurrence matrix, binary image method and fractal model. Through the feature extraction of corrosion morphology, the quantitative analysis of corrosion morphology and the microscopic evaluation of corrosion characteristics are achieved. The image recognition data are consistent with the electrochemical result for most cases, which confirms the validity of this image intelligent recognition method. The average gray value and energy value of corrosion morphology reduces with the Cl− concentration, indicating that the corrosion damage aggravates gradually. The increasing standard deviation and entropy reflects that the randomness of the pit distribution increases. The pitting ratio increases from 20.19% to 51.64% as the Cl− concentration increases from 50% to 200% of the standard solution. However, there exists a discrepancy for high Cl− concentration because of the irregular corrosion morphology and various pit depth. The fractal dimension increases with the complexity of the corroded surface at low Cl− concentration, but the fractal dimension decreases at high Cl− concentration because the corrosion complexity is interfered by the interconnection of corrosion holes due to the accelerated pit evolution.

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