# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 4 Conclusions  
In this study, we propose YOLOv8s-UEC, a network for underwater defect detection in concrete dams based on the YOLOv8s object detection framework. This model combines an improved Efficient-RepGFPN structure with a P2 detection layer as the neck of the model, replaces the bottleneck of the C2f module in the backbone with the ConvNeXt module, and substitutes the localization loss function with Focal-EIoU. Additionally, due to the time-consuming and costly nature of acquiring underwater defect images, which leads to a scarcity of such data, we constructed defect concrete walls with artificially created samples of cracks, spalling, and exposed bars. We collected underwater defect images using ROVs to create a dataset for network training, addressing the shortage and lack of diversity in underwater defect samples, and providing solid data support for future research in this area.  
YOLOv8s-UEC was proposed after systematically testing and evaluating various structures and modules through ablation study. Compared to other models, YOLOv8s-UEC significantly enhanced the defect feature extraction capability in underwater scenes. In the three common categories of underwater defect detection, \(F_{1}\), \(mAP_{0.5}\), and \(mAP_{0.50.95}\) increased by 2.15%, 1.4%, and 5.8%, respectively. The model's processing speed reached 107 FPS, improving detection accuracy and effectiveness while meeting real-time detection requirements. Validation results on the test set showed improved detection of small targets like underwater cracks; the predicted bounding boxes for various underwater defects were more reasonable; and when detecting the same target, the proposed model exhibited higher confidence. Thus, the proposed model is more suitable for underwater defect detection tasks compared to conventional algorithms, making it practically significant for the underwater safety assessment of dams.  
In the future, we will further explore the development of underwater defect detection. We plan to utilize underwater robotic equipment to capture real underwater defect images of dams, aiming to create a more generalized defect dataset. Additionally, we will apply pruning or knowledge distillation to the proposed model to reduce the number of parameters and lower the resource requirements for deployment on mobile platforms.  
C.L.: conceptualization, methodology, software, investigation, writing--original draft. Y.Z.: methodology, software, experiments, writing--original draft. F.K.: resources, supervision, project administration, funding acquisition, review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFB4703404), the National Natural Science Foundation of China (52079022), the Guangxi Key Technologies R&D Program (Gui Ke AB24010003).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the CCCC Second Harbor Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.