Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications
Abstract
:1. Introduction
2. General Steps in Image Recognition Techniques
2.1. Traditional Image Recognition Steps
2.1.1. Image Acquisition Collection
2.1.2. Image Pre-Processing
2.1.3. Feature Extraction
2.1.4. Image Classification
2.2. Deep Learning-Based Image Recognition
3. Identification of Metals
3.1. Traditional Methods
3.1.1. Wavelet Transform-Based Approach
3.1.2. Approaches Based on Fractal Theory
3.1.3. Methods Based on Grayscale Images
3.2. Deep Learning-Based Approach
4. Recognition and Mining of Corrosion Data Images
5. Identification of Coatings
5.1. Traditional Methods
5.2. Deep Learning Image Recognition Algorithm
6. Future Outlook
7. Conclusions
- Traditional methods have been effectively used for detecting cracks, pitting, and other defects in steel structures. By analyzing collected images of corroded steel, extracting corrosion features, and applying mathematical methods to mine the deep features of grayscale images, it is possible to distinguish between various corrosion morphologies. However, these models often lack generalization, meaning that changes in the environment and the recognition object can greatly affect the accuracy. Additionally, selecting the right model and extracting features can be complex. Deep learning methods, which require a large number of steel surface topography images, offer a solution where different application scenarios can use the same model, albeit with different datasets.
- By analyzing images of changes in electrochemical parameters during metal corrosion, deeper corrosion information can be extracted, such as determining equivalent circuit diagrams and predicting the lifetime of coatings.
- Traditional image recognition methods for coatings are similar to those used for metals. However, deep learning image recognition algorithms show more potential in recognizing the boundary, thickness, and uniformity of coatings.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Materials | Main Content | Author/Ref. | Year |
---|---|---|---|---|
Method based on wavelet transform | Al 7075-T76 | The electrochemical noise signal was analyzed to reveal the characteristics of the potential signal in detail and monitor the occurrence of local corrosion | Liu [23] | 2001 |
Aero-aluminum alloy | The formation and growth of corrosion pits were modeled and simulated. The corroded panels were evaluated by wavelet-based image processing | Pidaparti [53] | 2007 | |
Weathering steel | The corrosion state of weathering-resistant steel was evaluated by combining wavelet transform with the PSO-SVM technique | Yan [50] | 2014 | |
Methods based on fractal theory | Inconel alloy 600 | The fractal dimension of pitting pits hardly changes with temperature | Park [56] | 2003 |
Reinforced Concrete (RC) | The relationship between the crack fractal dimension, corrosion rate and steel bar diameter was established to evaluate the corrosion behavior of steel bars in degraded reinforced concrete structures | Li [57] | 2022 | |
X80 steel | The two-dimensional/three-dimensional fractal dimension of corrosion pit increased with the increase of AC density, showing linear and exponential relationships, respectively | Fu [58] | 2019 | |
Method based on grayscale | Galvanized high strength steel | The elliptic parameters of the binary image of the corrosion pit were extracted, and it was found that the elliptic parameters of the corrosion pit satisfy the Gaussian distribution along all directions | Xu [59] | 2016 |
Method | Advantage | Disadvantage |
---|---|---|
Methods based on wavelet variations | The low-frequency region responds to the color spatial distribution and brightness differences of the image, while the high-frequency region is able to respond to the local features of corrosion. | Differences in the calculation of wavelet energy entropy may lead to differences in corrosion image processing |
Approach based on typing theory | It has the ability to recognize features of non-traditional Euclidean geometry in an image and to distinguish differences in roughness in image information. | The fractal dimension may be the same for different images and cannot be used alone as a criterion for judging images |
Gray scale image based approach | It has the ability to reduce complexity, increase computing speed, and improve detail clarity. | Incomplete representation of information and inability to process corrosion with color characteristics |
Method | Concrete Algorithm | Materials | Main Content | Ref. |
---|---|---|---|---|
Traditional method | Method based on grayscale | Alkyd paint | The bubble size and area ratio are determined based on the gray image, so as to determine the bubble grade | [45] |
Method based on color space | Coatings on offshore wind platforms | Quantitative recording and rating of coating deterioration processes | [52] | |
Method based on grayscale | Coatings on steel bridge | Multiresolution pattern classification (MPC) was used to analyze the surface coating images of steel bridges, and the proportion of coating and rust in gray images was calculated | [64] | |
Methods based on deep learning | Deep transfer learning techniques | The coating on the ballast tank | An AI-based aided CBC evaluation system (A-CAS) was developed to identify different types of coating damage and corrosion | [66] |
Convolutional neural networks (CNN) | PVAsilver nanoparticles (nAg/PVA) | The CNN data were compared with electrochemical measurements and scanning electron microscopy (SEM) data | [69] |
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Wang, X.; Zhang, W.; Lin, Z.; Li, H.; Zhang, Y.; Quan, W.; Chen, Z.; You, X.; Zeng, Y.; Wang, G.; et al. Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications. Coatings 2024, 14, 1051. https://doi.org/10.3390/coatings14081051
Wang X, Zhang W, Lin Z, Li H, Zhang Y, Quan W, Chen Z, You X, Zeng Y, Wang G, et al. Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications. Coatings. 2024; 14(8):1051. https://doi.org/10.3390/coatings14081051
Chicago/Turabian StyleWang, Xinran, Wei Zhang, Zhifeng Lin, Haojie Li, Yuanqing Zhang, Weiyin Quan, Zhiwei Chen, Xueqiang You, Yang Zeng, Gang Wang, and et al. 2024. "Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications" Coatings 14, no. 8: 1051. https://doi.org/10.3390/coatings14081051