# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 1 Introduction  
Reservoirs and dams serve essential functions, including flood control, power generation, water supply, and irrigation, playing a vital role in safeguarding lives and fostering economic development [1; 2; 3]. However, during operation, concrete dams are subjected to foundation loads and sudden disasters, leading to noticeable surface defects, such as cracks. These defects not only compromise structural integrity but may also trigger other issues that pose potential hazards to safety [4; 5; 6; 7], even increasing the risk of dam failure. Therefore, regular defect detection of the dam face is essential [8; 9; 10]. Since most of the dam structure is located under the water surface for a long time, the detection of the underwater part is more difficult [11; 12]. Traditional underwater defect detection primarily relies on professional divers wearing diving equipment to carry out operations underwater [13]. However, constrained by the underwater environment and water depth, this method is highly dangerous and unable to work in deep water [11; 14]. With the development and maturation of remotely operated vehicle (ROV) technology, it has successfully replaced human divers for underwater inspections in hazardous deep-water environments due to its strong adaptability, high pressure resistance, and ability to operate for extended periods [15; 16; 17; 18]. ROV can carry multi-source sensors, using a variety of advanced technical means, such as underwater camera, sonar detection, remote sensing technology, and robotic arms, to carry out a comprehensive detection and assessment of the dam [19; 20; 21; 22]. Many countries and organizations around the world have successfully applied ROV technology to realize underwater exploration missions for dams. As early as 2001, Japan developed and applied its own underwater exploration ROV to the Miyase Dam field test, and the ROV moved 50 m between the concrete of the dam wall deep in the dam to observe its stratification [23]. The University of Girona in Spain uses its own developed underwater robot equipped with side-scan sonar, speed sensors, underwatercameras, and imaging sonars to inspect the underwater surface of dams and perform underwater image stitching. They observed a significant amount of underwater algae, confirming the feasibility of ROVs for dam surface inspections [24]. The Tokai University in Japan has designed a negative pressure suction cup ROV that can adhere to dam surfaces and move vertically along them using rubber wheels when close to the dam. This design enhances the stability of the ROV during underwater inspections [25]. China used an underwater ROV to detect the upstream dam face of the Tongzilin Dam and to detect underwater seepage through multibeam sonar [26]. The TB-1 ROV developed by Harbin Engineering University successfully observed centimeter-level underwater dam face cracks in the underwater multi-angle detection test for the Gezhouba Dam, which significantly improved the accuracy of the underwater robot detection [27]. With the development of machine vision technology, deep learning methods provide new ideas for underwater detection. Deep learning is a part of machine learning, and deep learning achieves the learning of data features and reveals their intrinsic connections by constructing an artificial neural network and simulating the information transfer between neurons [28, 29]. Deep learning algorithms have numerous advantages, including strong feature extraction capabilities, high accuracy in recognition, and good real-time performance [30, 31]. By using imaging devices like cameras to collect image data and analyzing it through machine vision, deep learning can effectively carry out large-scale structural defect detection tasks. In underwater detection, underwater defect images can be acquired by ROV and defect detection algorithms can be constructed by deep learning [32, 33, 34].