# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Image Processing of Underwater Cracks
#### 4.2.2 Underwater Crack Image Recognition
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author and Year** & **Approaches/Methods** & **Results** \\ \hline [PERSON] et al. [46], 2023 & Affine shadow transform-based methods and adaptive histogram equalization & The processed image gets uniform illumination. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline [PERSON] et al. [71], 2018 & Coarse set-based methods & Obtained an image with balanced background lighting. \\ \hline [PERSON] 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. \\ \hline [PERSON] 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. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Preprocessing methods and results for underwater crack images. preprocessing techniques for underwater crack images on dam surfaces, most preprocessed underwater crack images conform to the prior knowledge of the crack. In addition, the improvement over the conventional method makes it possible to identify underwater cracks using the conventional method. With the development of computer computing capabilities, deep learning algorithms have been used to extract underwater cracks on dam surfaces. Deep learning methods process unknown images based on the results of a large amount of statistical data [79]. However, due to the complexity of the underwater environment and the limitation of underwater photography technology, there are few effective underwater crack images of dam face, and the corresponding image label data are also lacking. Moreover, obtaining the best performing network model is based on repeated attempts with different network structures and repeated tuning of the parameters. Therefore, training a new network model from scratch significantly increases the computational burden [80]. Transfer learning [81] perfectly solves the aforementioned problems of a small amount of data and heavy computational burden by transferring the knowledge learned from the benchmark dataset to the new problem. [PERSON] et al. [72] applied prior knowledge obtained from the source domain to underwater crack image segmentation by using a multi-level adversarial transfer network to reduce the data labeling effort, and integrated an attention mechanism into the segmentation network to achieve higher segmentation accuracy. [PERSON] et al. [37] overcame the problem of insufficiently labeled data by using a two-stage hybrid transfer learning approach and using a lightweight semantic segmentation network for real-time segmentation of underwater cracks on dam surfaces. Due to the reduction of the workload of underwater image acquisition and the calculation of network training, the application of the above methods further advances the development of deep learning for underwater crack detection on dam surfaces. [PERSON] et al. [26] proposed a crack detection method to detect large-scale underwater cracks based on graph convolutional neural network. [PERSON] et al. [82] used a lightweight deep convolutional network to detect underwater cracks and achieved high performance.