# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 2 Methodology
### Proposed YOLOv8s-UEC Network  
The structure of YOLOv8 is designed to be applicable across various object detection tasks, resulting in a balanced overall performance. However, in underwater defect detection tasks, challenges arise due to the influence of the underwater environment. Defect features on underwater dam surfaces are not distinct and are often intertwined with complex backgrounds, making them difficult to discern. Defects like cracks and spalling affect each other, leading to significant differences in pixel proportions in images, and various defects have  
Figure 5: Structure of Efficient-RepGFPN.  
Figure 6: Structure of Efficient RepGFPN feature fusion module CSPStage.  
different texture and shape characteristics. These factors present significant challenges for detection. To address the issues of indistinct defect features and blurry boundaries between defects and backgrounds, it is essential to enhance the network's feature extraction capability. This capability is closely related to the network's structure and modules; therefore, improving these can effectively boost network performance. Additionally, it is crucial to balance detection accuracy and speed during improvements to avoid sacrificing detection speed due to the addition of excessive modules for accuracy enhancement. Thus, a gradual debugging process is necessary to achieve the best balance between these two aspects.  
Another challenge in underwater detection is the significant disparity in pixel proportions occupied by different defects. For example, cracks occupy a small proportion of the image compared to large areas of underwater concrete spalling, falling into the small target category. Small targets are often overlooked or misjudged due to their minimal pixel presence, leading to lower detection accuracy. To improve detection accuracy for small targets, the introduction of the P2 small object detection layer can be beneficial. The P2 layer typically has higher resolution, allowing the model to better capture the details of small-sized targets. Higher resolution feature maps provide more spatial information, aiding in detecting small objects. Moreover, because the P2 layer is in a shallower network position, it can capture finer details, which is crucial for understanding the shape and texture of small targets.  
The specific improvement measures are as follows: (1) replacing the neck part of the network with Efficient-RepGFPN to flexibly control the expression ability of high and low layers of features, improve the feature extraction ability of the network, and solve the problem of blurring the boundary between the defects and the background, and the problem of low accuracy caused by the features that are not obvious; (2) introducing the P2 small target detection layer into the Efficient-RepGFPN structure improves detection accuracy for small targets like cracks. This approach addresses the issue of significant differences in pixel ratios among various defects in images, helping to reduce the chances of small target defects being overlooked or misclassified; (3) replacing the bottleneck in the C2f module in the network backbone with a ConvNeXt pure convolutional lightweight high-performance structure, to improve the detection efficiency. The proposed network is referred to as YOLOv8s-UEC (YOLOv8s for underwater defect detection based on improved Efficient-RepGFPN and ConvNeXt Block), as shown in Figure 7.  
Figure 7: Proposed underwater defect detection network YOLOv8s-UEC.