# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 1 Introduction  
As one of the infrastructures of water conservancy and hydropower projects, dams play a huge role in flood control and disaster reduction, hydroelectric power generation, the irrigation of agricultural land, and water resource allocation [1; 2; 3]. At present, most dams are aging over a long period of use, increasing the risk of failure [3; 4; 5]. It is of great significance to carry out regular maintenance and inspection of dams to ensure their safe operation and reduce the overall repair and maintenance costs of dams. During the long-term use of dams, there will be damages such as cracks, spalling, erosion, voids, and wear [6]. These defects seriously affect the safe and stable operation of the dam. Among these damages, cracks have attracted widespread attention because of their susceptibility, hazards, and inducing effects on other disasters. Most of the cracks originate on the surface of the structure, destroying the integrity of the structure and reducing its capacity for bearing [7; 8; 9; 10]. Therefore, timely and accurate detection of cracks is an urgent problem to be solved in water conservancy and hydropower projects. At present, most of the crack detection for dams mainly focuses on the above-water part, and cracks are detected by technologies such as ultrasonic [11], electromagnetic wave [12], and sensing equipment [13; 14]. In recent years, unmanned aerial vehicles (UAVs) combined with computer vision technology have been applied to dam damage detection. UAVs [15; 16]are used to collect dam damage images, and computer vision methods [17; 18] are used to identify the dam's damage through images. Compared with the above-water concrete dam surface, the underwater concrete dam surface is more prone to cracks due to long-term immersion in water and the long-term coupling of temperature changes, water chemical corrosion, hydraulic fracturing, and other factors [19; 20; 21; 22]. The underwater cracks of concrete dams not only exists on the surface, but also easily extend to the interior of the dam by the action of hydraulic fracturing, thus destroying the overall stability of the dam and causing immeasurable consequences. Therefore, it is necessary to regularly check the underwater cracks of concrete dams to reduce the impact of underwater dam surface cracks. However, due to the danger of underwater operation and the complexity of the underwater environment around dams, the detection is more difficult. When selecting the detection equipment, not only the applicability of the equipment, but also the water resistance should be considered. Existing detection methods mainly include sensor-based detection [23; 24] and image-based detection [25; 26]. However, long-term underwater operation greatly limits the practical application of embedded sensors. As an efficient detection technology for concrete dam surface cracks, the image detection method is widely used in crack detection of above-water dams. However, biofouling, insufficient light, etc., lead to complex underwater environments; thus, it is difficult for visual sensors to approach the underwater dam surface, which increases the difficulty of obtaining underwater images. Therefore, this method is also limited in use. In recent years, some researchers have made review works of crack detection technologies and summarized the advanced technologies of crack detection. [PERSON] et al. [27] introduced the surface crack detection method of transportation infrastructure based on machine vision. [PERSON] et al. [28] summarized the crack detection technologies of civil infrastructures from the perspective of sensing approaches and data analysis approaches. [PERSON] et al. [29] presented crack detection, classification, and quantification in asphalt pavement using image processing methods. [PERSON] et al. [30] reviewed concrete crack detection schemes based on image processing techniques. [PERSON] and [PERSON] [31] organized crack detection algorithms based on machine learning and compared the performance of different models.