# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 1 Introduction
The better the diversity of data used for learning, the better the network training effect, and the higher the detection performance of the algorithm [35, 36, 37, 31]. However, due to the underwater environment, the acquisition of concrete underwater defect images requires a lot of time and expensive equipment, the current underwater defect images are very scarce, and the corresponding image data labels for training are also more scarce [18, 38]. Some scholars have considered transfer learning to solve this problem. Transfer learning [39, 40] solves the problem of insufficient data and high workload by transferring the knowledge learnt from a baseline dataset to a new problem. [PERSON] et al. [41] proposed an underwater dam crack image segmentation model based on transfer learning, which effectively reduces the workload of data labeling and achieves better segmentation results for underwater dam crack images compared to existing methods, although it suffers from poor real-time performance. [PERSON] et al. [38] introduced a two-stage joint recognition method that transfers crack feature knowledge learned from civilian infrastructure to underwater dam structures, developing a real-time segmentation framework for underwater dam cracks, achieving an accuracy of up to 0.9444 on the test set and effectively estimating the geometric features and sizes of the cracks. [PERSON] et al. [42] proposed a method for underwater crack detection based on image stitching and semantic segmentation, which successfully improved m-IOU and F1 scores and demonstrated good generalization capability. [PERSON] et al. [43] developed an underwater crack segmentation network that combines convolutional neural networks and the Otsu algorithm, enabling crack recognition and measurement through preprocessing of underwater images, with a recognition precision of 0.2 mm, surpassing other similar methods. [PERSON] et al. [44] proposed a crack disease recognition network for underwater bridge structures based on an improved YOLOv4, with the model size only one-fifth of the original, enhancing detection efficiency and ease of deployment. Despite these studies addressing underwater defect issues and proposing solutions like transfer learning, the lack of underwater defect image samples remains unchanged, which continues to restrict the further development of underwater defect detection technology. Therefore, constructing a dataset of underwater defects for deep learning would significantly aid research in underwater defect detection. At the same time, there are unique features of underwater defects in concrete dams, such as cracking, spalling, and exposed bars [11, 16]. Due to the influence of the underwater environment, defect features on dam surfaces are not obvious and are difficult to distinguish from complex backgrounds, as shown in Figure 1a. Additionally, defects such as cracks and spalling affect each other, resulting in significant differences in the pixel ratios they occupy in the images. The texture and morphological characteristics of different defects also vary, as illustrated in Figure 1b. All the above create great difficulties for underwater defect recognition based on object detection. Therefore, it is necessary to develop an algorithm with high detection accuracy for underwater defect features to achieve multiple underwater defect detection. The contributions of this study are as follows. First, an underwater defect dataset was established by artificially creating defect specimens and using an ROV for image acquisition, which included cracks, spalling, and exposed areas. Second, model ablation experiments were conducted to address the characteristics of underwater defects, gradually improving the YOLOv8s network by introducing the Efficient-RepGFPN structure and ConvNeXt module, leading to the proposal of the YOLOv8s-UEC network for underwater defect detection. Finally, the performance of the proposed network was tested, demonstrating the feasibility and superiority of the algorithm.