A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
Abstract
:1. Introduction
2. Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams
2.1. Manual Visual Inspection
2.2. Intelligent Monitoring Techniques
2.2.1. Acoustic and Vibration Methods
2.2.2. Electromagnetic Methods
2.2.3. Temperature Tracing Method
2.3. Digital Image Detection Method
3. Dam Crack Detection Based on Underwater Robots
3.1. Underwater Robots
3.2. Application of Underwater Robots in Dam Crack Detection
4. Application of Image Processing Techniques to Underwater Crack Detection in Dams
4.1. Problems in Underwater Image Processing
4.2. Image Processing of Underwater Cracks
4.2.1. Underwater Crack Image Preprocessing
4.2.2. Underwater Crack Image Recognition
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author and Year | Approaches/Methods | Results |
---|---|---|
Xin et al. [46], 2023 | Affine shadow transform-based methods and adaptive histogram equalization | The processed image gets uniform illumination. |
Ma et al. [32], 2022 | Affine shadow transform-based methods | The color deviation of the processed image is small and the definition of the processed image is improved. |
Wan et al. [76], 2019 | Methods based on biological principles | The image adaptively homogenizes the global brightness of the image according to its overall brightness distribution without human intervention. |
Zhang et al. [74], 2018 | Adaptive enhancement method based on the principle of biological vision | The proposed method enhances the grayscale contrast at the edges of faint objects at different brightness conditions. |
Fan et al. [71], 2018 | Coarse set-based methods | Obtained an image with balanced background lighting. |
Shi et al. [25], 2016 | Affine shadow transform-based methods | The proposed method eliminates the inhomogeneous illumination and well-preserves the crack texture in the image. |
Ma et al. [75], 2016 | Dark channel-based enhancement algorithms | The proposed method can effectively suppress the noise interference of underwater images and improve the clarity of underwater dam crack images. |
Author and Year | Approaches/Methods | Results |
---|---|---|
Xin et al. [46], 2023 | Method of combining local features with global features | The proposed method can detect cracks well in low contrast, complex backgrounds, and uneven illumination conditions. |
Ma et al. [32], 2022 | Binocular vision method | The proposed method can quickly determine the crack width. |
Cao and Li [26], 2022 | An improved As-Projective-As-Possible algorithm and graph convolutional neural network | The proposed method achieves stitching of small images to obtain the full shape of underwater cracks. The image segmentation algorithm is highly accurate for cracks in different regions, different water depths, and different degrees of deformation. |
Li et al. [37], 2022 | Lightweight semantic segmentation network and two-stage hybrid transfer learning algorithm | The proposed method enables the construction of pixel-by-pixel segmentation models of underwater cracks in the presence of limited samples. |
Fan et al. [72], 2022 | Multilevel antagonism transfer network and improved U-net image segmentation network | The proposed method achieves accurate segmentation of underwater dam crack images, but its real-time performance is poor. |
Qi et al. [83], 2022 | Convolution neural network and Ostu algorithm | The proposed method can efficiently detect and localize cracks in underwater optical images at low illumination, low signal-to-noise ratio, and low contrast. |
Mucolli et al. [20], 2019 | Local features: Haralick texture features | The proposed method has high accuracy, robustness to illumination, and reasonable computational efficiency. |
Wan et al. [76], 2019 | Binocular vision method | The proposed method can obtain the spatial information of defects. |
Fan et al. [71], 2018 | Methods of combining local features with global features | The proposed method can effectively detect cracks in complex environments without supervision. |
Zhang et al. [74], 2018 | Edge detection model based on artificial bee colony algorithm | The proposed method can detect dam crack defects in complex underwater environments. |
Shi et al. [53], 2017 | Crack detection algorithm based on clustering analysis and tensor voting | The proposed method can accurately and efficiently detect and classify underwater dam cracks in complex underwater environments based on sonar images. |
Shi et al. [25], 2016 | Method of combining local features with global features | The proposed method can accurately and efficiently detect and classify underwater dam cracks in complex underwater environments. |
Chen et al. [77], 2012 | Adaptive underwater dam surface edge detection algorithm based on multi-structure and multi-scale elements | The proposed method can effectively remove noise and maintain image edge details. |
Chen et al. [78], 2012 | Morphology-based method | This method enables accurate and efficient detection and classification of underwater dam cracks in complex underwater environments. |
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Chen, D.; Huang, B.; Kang, F. A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces. Appl. Sci. 2023, 13, 3564. https://doi.org/10.3390/app13063564
Chen D, Huang B, Kang F. A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces. Applied Sciences. 2023; 13(6):3564. https://doi.org/10.3390/app13063564
Chicago/Turabian StyleChen, Dong, Ben Huang, and Fei Kang. 2023. "A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces" Applied Sciences 13, no. 6: 3564. https://doi.org/10.3390/app13063564
APA StyleChen, D., Huang, B., & Kang, F. (2023). A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces. Applied Sciences, 13(6), 3564. https://doi.org/10.3390/app13063564