## 1 Introduction  
The rapid development of the water industry has seen more and more bridges and dams being constructed. These buildings are susceptible to cracks in their structures due to adverse factors such as ageing materials, hydraulic fracturing and water chemical corrosion, which in turn accelerate the damage to the buildings [1; 2]. Reliable and effective detection of cracks in buildings, as well as reinforcement and repair of buildings, is essential to ensure their proper use [3]. Manual inspection has become the traditional solution for detecting cracks in dams, but with a large number of bridges and dams, detecting cracks in the main structure of bridges requires aerial work, and detecting underwater cracks in dams requires emptying the reservoir, making traditional manual inspection methods time consuming and a security risk. The need for aerial and underwater operations has led to the development of remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) [4; 5; 6]. ROVs and UAVs are often equipped with high resolution visible light cameras, self-contained light sources and some data storage. They are capable of replacing manual inspection for a wide range of underwater operations and aerial operations in harsh environments. Figure 1 shows a remotely operated vehicle in operation. During a complete ROVs or UAVs inspection mission, numerous images and videos related to structural damage to buildings can be obtained. However, relying solely on manual observation to extract damage-related information from this video data is a costly method. In addition, the accuracy of manual observation results depends on subjective human judgement. Manual observations have a high rate of misjudgment due to the complex underwater filming environment. Combining ROVs and UAVs with computer vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate crack detection. Vision-based crack detection methods fall into two routes, one based on image processing techniques and the other on deep learning techniques. Image-processing-based crack detection techniques often binarize image pixels according to specific rules in order to distinguish cracked areas and non-cracked areas. Reference [7] summarises the history and implementation of image-processing-based crack detection methods and divides traditional rule-driven crack detection methods into threshold-based crack detection methods and edge-based crack detection methods. Traditional crack detection algorithms usually require pre-processing of images such as denoising, and it is difficult for a single algorithm to accurately extract crack features, often requiring a combination of multiple algorithms, which is computationally expensive, slow to detect and does not have real-time detection capability. The rapid development of deep learning techniques has led to the rapid development of computer vision tasks such as object detection [8], semantic segmentation [9] and instance segmentation [10; 11]. Deep-learning-based crack detection methods have also gained rapid development. Deep-learning-based crack detection algorithms achieve accurate detection of cracks in buildings by learning crack features from a large number of crack images and capturing the features of cracks in different forms and different contexts. This method has the advantages of high accuracy and good real-time performance. Reference [12] proposed an automatic concrete defect identification method based on convolutional neural network models and interpreted the obtained results in a form that is humanly explainable. Reference [13] proposed a concrete defect identification method based on a one-stage object detection model, which had high accuracy and real-time detection capabilities. References [14; 15] provide detailed evaluations of the performance of object detection models and semantic segmentation models for automated detection.