# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Problems in Underwater Image Processing  
The complexity of the underwater environment and the specificity of the medium lead to certain degradations of underwater images, such as image blur, low contrast, color distortion, and other problems. Figure 6 is a comparison of crack images taken in the atmosphere and in water. The two images are quite different due to the different shooting scenarios. It can be seen that the recognition difficulty of underwater images is greatly increased due to the blurred edges of the cracks and the low signal-to-noise ratio by comparison. After years of development, significant progress has been made in the detection of cracks on the surface of concrete dams using image processing techniques, mainly including traditional methods and deep learning-based methods. However, the complex imaging environment of underwater images and the low quality of underwater images make it difficult for these methods to be directly applied to underwater crack detection on dam surfaces. Figure 5: Methods for underwater crack detection based on underwater robots and digital image processing. Figure 6: Crack image: (**a**) crack image taken in the air; (**b**) crack image taken underwater. Traditional digital image processing methods have difficulty handling underwater images. Traditional methods for image crack detection mainly include threshold-based methods [68], edge detection methods [69], and methods based on local [25; 70] or global features [71]. Figure 7 shows a comparison of the characteristics of above-water and underwater cracks on the surface of dams, with images of above-water cracks on the left and images of underwater cracks on the right. Threshold-based methods detect cracks by the difference between the gray value of the crack region and the surrounding region. However, in underwater images, inhomogeneous illumination, blurring noise, and shadows make the difference between the gray value of the crack and the background region not obvious, which leads to the difficulty of crack recognition. In the image of the above-water crack on the dam surface shown in Figure 7a, the gray values in the crack region A\({}_{\text{at}}\) and in the background region are quite different, while the gray values in the regions C\({}_{\text{wt}}\) and D\({}_{\text{wt}}\), far from the source, do not differ much from those in crack regions A\({}_{\text{wt}}\) and B\({}_{\text{wt}}\) in Figure 7b. Therefore, for underwater images, the crack region and the background region cannot be directly distinguished based on the gray value alone. Edge detection is performed by computing the first or second derivative to determine the edge position based on the principle that the gray value at the edge of the crack changes abruptly. However, for underwater images, there is no obvious edge feature at the crack location due to the low contrast of the image. As shown in Figure 7c,d, the edge feature of the crack area A\({}_{\text{ae}}\) of the image in the atmosphere (Figure 7c) is obvious, while the gray value at the edge A\({}_{\text{we}}\) of the crack area of the underwater image (Figure 7d) has no obvious change. It is obviously unreasonable to detect the edge of an underwater crack image directly through an edge detection algorithm. Methods based on global or local features are also no longer applicable due to many factors, such as underwater image noise and image distortion. As shown in Figure 7e,f, the noise of the background areas A\({}_{\text{ac}}\) and B\({}_{\text{ac}}\) of the atmospheric image (Figure 7e) is significantly different from the crack features, while the underwater image (Figure 7f) has areas similar to cracks at the background area A\({}_{\text{wc}}\), which leads to a misjudgment of the crack region and reduces the accuracy of crack detection.