# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 5 Summary and Conclusions  
During the long-term operation of dams, there will be cracks, erosion, spalling, leakage, and other defects, among which cracks will destroy the integrity of the structure, reduce the bearing capacity of the structure, and seriously threaten the safety of the dam. Therefore, in order to ensure the safe and stable operation of dams, it is necessary to regularly conduct comprehensive crack detection on the dam to observe the development of cracks. However, due to the particularity and complexity of the deep-water environment, the underwater crack detection of dam surfaces is more difficult to carry out. Research on dam crack detection for underwater environments is actively being carried out. Acoustic and \begin{table}
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**Author and Year** & **Approaches/Methods** & **Results** \\ \hline [PERSON] 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. \\ \hline [PERSON] et al. [32], 2022 & Binocular vision method & The proposed method can quickly determine the crack width. \\ \hline [PERSON] and [PERSON] [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. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] et al. [20], 2019 & Local features: Haralick texture features & The proposed method has high accuracy, robustness to illumination, and reasonable computational efficiency. \\ \hline [PERSON] et al. [76], 2019 & Binocular vision method & The proposed method can obtain the spatial information of defects. \\ \hline [PERSON] et al. [71], 2018 & Methods of combining local features with global features & The proposed method can effectively detect cracks in complex environments without supervision. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] et al. [78], 2012 & Morphology-based method & This method enables accurate and efficient detection and classification of underwater dam cracks in complex underwater environments. \\ \hline \hline \end{tabular}
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