Early Corrosion Behavior of E690 Steel in the Tropical Atmospheric Environment Based on Corrosion Image Recognition Technology

HU Jiezhen; YANG Jingrong; DENG Peichang; LAN Wenjie; HOU Mingxin; WEN Cheng

doi:10.7643/ issn.1672-9242.2026.05.012

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Equipment Environmental Engineering ›› 2026, Vol. 23 ›› Issue (5) : 102-111. DOI: 10.7643/ issn.1672-9242.2026.05.012

Early Corrosion Behavior of E690 Steel in the Tropical Atmospheric Environment Based on Corrosion Image Recognition Technology

HU Jiezhen^1a,2, YANG Jingrong^1a, DENG Peichang^1b*, LAN Wenjie^1a, HOU Mingxin^1a, WEN Cheng^1a

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Abstract

The work aims to investigate the early corrosion behavior of E690 steel in the tropical atmospheric environment. A corrosion image recognition technology was combined with the weight loss method, electrochemical testing, and morphological observation to obtain surface corrosion morphology images of E690 steel with a UAV image acquisition system during atmospheric exposure. The grey threshold was determined by the grey histogram method, and the images were binarised to obtain the corrosion product coverage on the steel surface. Combined with the binarisation results, corrosion product coverage, open circuit potential, corrosion rate, and cross-sectional morphology, the early corrosion evolution of E690 steel in atmospheric environment was analyzed. After exposure of E690 steel to the tropical atmospheric environment, the corrosion products on the steel surface increased continuously with exposure time, and the corrosion product coverage reached 72.4% after 7 days of exposure. After 30 days of exposure, the specimen surface was almost fully covered by corrosion products, the corrosion product coverage reached 95.2%, and the variation in coverage became slow. The corrosion rate gradually decreased as the exposure time increased, the corrosion rate changed little, and the corrosion tended to stabilize after 30 days of exposure. The corrosion image recognition technology can effectively reflect the evolution of early corrosion behavior of E690 steel in the tropical atmospheric environment and quantitatively characterize the distribution of surface corrosion products. The obtained results are consistent with those from the weight loss method, electrochemical testing, and cross-sectional morphology analysis, and can be used to evaluate and predict the early corrosion behavior of E690 steel in the atmospheric environment.

Key words

corrosion morphology / image segmentation / E690 steel / atmospheric environment / corrosion product coverage

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HU Jiezhen, YANG Jingrong, DENG Peichang, LAN Wenjie, HOU Mingxin, WEN Cheng. Early Corrosion Behavior of E690 Steel in the Tropical Atmospheric Environment Based on Corrosion Image Recognition Technology[J]. Equipment Environmental Engineering. 2026, 23(5): 102-111 https://doi.org/10.7643/ issn.1672-9242.2026.05.012

References

[1] 高君懿, 徐玮辰, 张斌斌, 等. 耐候钢锈层在大气环境中的防护机制及其稳定化处理技术[J]. 工程科学学报, 2025, 47(5): 1032-1046.
GAO J Y, XU W C, ZHANG B B, et al.Protective Mechanism of Weathering Steel Rust Layers in Atmospheric Environments and Their Stabilization Treatment Technologies[J]. Chinese Journal of Engineering, 2025, 47(5): 1032-1046.
[2] 王红, 李兵, 杨文军, 等. 基于大气腐蚀阶段特征的钢结构寿命预测方法[J]. 腐蚀科学与防护技术, 2022, 34(3): 112-118.
WANG H, LI B, YANG W J, et al.Life Prediction Method of Steel Structures Based on Atmospheric Corrosion Stage Characteristics[J]. Corrosion Science and Protection Technology, 2022, 34(3): 112-118.
[3] 邢佩, 卢琳, 李晓刚. 海洋用高强钢E690氧浓差腐蚀行为研究[J]. 材料研究学报, 2016, 30(4): 241-247.
XING P, LU L, LI X G.Oxygen-Concentration Cell Induced Corrosion of E690 Steel for Ocean Platform[J]. Chinese Journal of Materials Research, 2016, 30(4): 241-247.
[4] 任凯, 郭成龙, 陈文. 海洋大气环境下合金钢锈层结构及其演化规律[J]. 表面技术, 2021, 50(6): 150-158.
REN K, GUO C L, CHEN W.Structure and Evolution of Rust Layers of Alloy Steel in Marine Atmospheric Environment[J]. Surface Technology, 2021, 50(6): 150-158.
[5] 刘志强, 王海涛, 赵勇. 钢铁材料大气腐蚀锈层结构与防护性能关系[J]. 中国腐蚀与防护学报, 2020, 40(4): 289-296.
LIU Z Q, WANG H T, ZHAO Y.Relationship between Rust Layer Structure and Protective Performance of Steels under Atmospheric Corrosion[J]. Journal of Corrosion and Protection, 2020, 40(4): 289-296.
[6] 李思明, 郝鹏飞. 锈层FeOOH物相演化及其对钢铁防护性能的影响[J]. 金属功能材料, 2022, 29(2): 45-50.
LI S M, HAO P F.Evolution of FeOOH Phases in Rust Layers and Their Influence on Corrosion Protection of Steels[J]. Functional Materials for Metals, 2022, 29(2): 45-50.
[7] 宋浩, 张兴, 李宇. 模拟海洋气氛中低合金钢的电化学腐蚀行为与锈蚀演化[J]. 腐蚀与防护, 2022, 43(10): 1011-1017.
SONG H, ZHANG X, LI Y.Electrochemical Corrosion Behavior and Rust Evolution of Low-Alloy Steel in Simulated Marine Atmosphere[J]. Corrosion & Protection, 2022, 43(10): 1011-1017.
[8] KHAYATAZAD M, DE PUE L, DE WAELE W.Detection of Corrosion on Steel Structures Using Automated Image Processing[J]. Developments in the Built Environment, 2020, 3: 100022.
[9] REN S B, KONG C, GU Y, et al.Measurement Pitting Morphology Characteristic of Corroded Steel Surface and Fractal Reconstruction Model[J]. Measurement, 2022, 190: 110678.
[10] 杜玉红, 张松奇. 基于红外图像的耐腐蚀船舶材料表面缺陷识别研究[J]. 舰船科学技术, 2023, 45(14): 152-155.
DU Y H, ZHANG S Q.Research on Surface Defect Identification of Corrosion-Resistant Ship Materials Based on Infrared Images[J]. Ship Science and Technology, 2023, 45(14): 152-155.
[11] ITZHAK B, GOUZMAN I.Image-Based Corrosion Detection Using Gray-Level Histogram Analysis[J]. Surface and Coatings Technology, 2020, 402: 126312.
[12] COSTA J M P M T. Fractal Analysis of Corrosion Image Boundaries[J]. Corrosion Engineering, Science and Technology, 2016, 51(2): 104-110.
[13] 张新新, 高志明, 胡文彬, 等. Q235钢在薄液膜下腐蚀行为与图像信息的相关性研究[J]. 中国腐蚀与防护学报, 2017, 37(5): 444-450.
ZHANG X X, GAO Z M, HU W B, et al.Correlation between Corrosion Behavior and Image Information of Q235 Steel beneath Thin Electrolyte Film[J]. Journal of Chinese Society for Corrosion and Protection, 2017, 37(5): 444-450.
[14] ZENG Z, CHEN J, LI B, et al.Gray-Level Histogram Entropy and Texture Features for Visual Corrosion Detection on Steel Surfaces[J]. Measurement, 2022, 198: 111385.
[15] 王清泉, 韩卓, 宋倩倩. 基于图像处理的船用钢材表面腐蚀区域识别研究[J]. 舰船科学技术, 2021, 43(7): 128-132.
WANG Q Q, HAN Z, SONG Q Q.Surface Corrosion Region Identification of Marine Steel Based on Image Processing[J]. Ship Science and Technology, 2021, 43(7): 128-132.
[16] 龙梦翔, 付桂翠, 万博, 等. 基于K-means++聚类算法和SSIM指标的金属板材腐蚀区域识别[J]. 中国腐蚀与防护学报, 2024, 44(2): 437-444.
LONG M X, FU G C, WAN B, et al.Corrosion Area Identification of Sheet Metal Based on K-means++ Clustering Algorithm and SSIM Index[J]. Journal of Chinese Society for Corrosion and Protection, 2024, 44(2): 437-444.
[17] RIBEIRO R B, SILVA J W J, HEIN L R O, et al. Morphology Characterisation of Pitting Corrosion on Sensitized Austenitic Stainless Steel by Digital Image Analysis[J]. ISRN Corrosion, 2013, 2013: 905942.
[18] 国家市场监督管理总局.金属和合金的腐蚀-大气腐蚀性GB/T 19292.1—2018 [S]. 北京: 中国国家标准化管理委员会, 2018.
State Administration for Market Regulation. Corrosion of Metals and Alloys-Atmospheric Corrosiveness: GB/T 19292.1—2018 [S]. Beijing: China Standardization Press, 2018.
[19] 国家市场监督管理总局. 金属和合金的腐蚀腐蚀试样上腐蚀产物的清除: GB/T 16545—2015[S]. 北京: 中国标准出版社, 2015.
State Administration for Market Regulation. Corrosion of Metals and Alloys—Removal of Corrosion Products from Corrosion Test Specimens: GB/T 16545—2015[S]. Beijing: China Standard Press, 2015.
[20] GONZALEZ R C, WOODS R E.Digital Image Processing[M]. 4th ed. New York: Pearson, 2018.
[21] HALLUR S, GAVADE A.Image Feature Extraction Techniques: A Comprehensive Review[J]. Franklin Open, 2025, 12: 100366.
[22] YU Y, WANG C P, FU Q, et al.Techniques and Challenges of Image Segmentation: A Review[J]. Electronics, 2023, 12(5): 1199.
[23] SUN R, LEI T, CHEN Q, et al.Survey of Image Edge Detection[J]. Frontiers in Signal Processing, 2022, 2: 826967.
[24] 兰红, 方治屿. 3维灰度矩阵的钢板缺陷图像识别[J]. 中国图象图形学报, 2019, 24(6): 859-869.
LAN H, FANG Z Y.Image Recognition of Steel Plate Defects Based on a 3D Gray Matrix[J]. Journal of Image and Graphics, 2019, 24(6): 859-869.
[25] 张旭昀, 贾蕊, 孙丽丽, 等. 基于CO₂腐蚀形貌特征的腐蚀预测方法研究[J]. 化工机械, 2012, 39(3): 347-350.
ZHANG X Y, JIA R, SUN L L, et al.Study on Corrosion Prediction Method Based on CO₂ Corrosive Morphology[J]. Chemical Engineering & Machinery, 2012, 39(3): 347-350.
[26] LEYGRAF C, WALLINDER I O, TIDBLAD J, et al.Atmospheric Corrosion[M]. New York, USA: John Wiley & Sons, 2016.
[27] MAMENG S H, PETTERSSON R, LEYGRAF C.Effect of Stainless Steel Composition on Atmospheric Corrosion Resistance at a Marine Site in Dubai[J]. Corrosion, 2017, 73(7): 880-891.
[28] WANG B Q, LIU L G, CHENG X Q, et al.Advanced Multi-Image Segmentation-Based Machine Learning Modeling Strategy for Corrosion Prediction and Rust Layer Performance Evaluation of Weathering Steel[J]. Corrosion Science, 2024, 237: 112334.
[29] ARIAS F, GUEVARA E, JARAMILLO E, et al.Automated Assessment of Marine Steel Corrosion Using Visible-Near-Infrared Hyperspectral Imaging[J]. Coatings, 2025, 15(6): 645.
[30] TAHERI-SHAKIB J, AL-MAYAH A.Dynamics of Localized Accelerated Corrosion in Reinforced Concrete: Voids, Corrosion Products, and Crack Formation[J]. Ceramics International, 2024, 50(22): 48755-48767.
[31] LIU H, ZHAO Q, GUAN Y, et al.Influence of FeOOH Phase Composition on Corrosion Resistance of Steel Exposed to Marine Atmosphere[J]. Materials Chemistry and Physics, 2023, 300: 127367.