Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC

This study proposes a concrete dam underwater apparent defect detection algorithm named YOLOv8s-UEC for intelligent identification of underwater defects. Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually constructing defective concrete walls for the training of defect detection networks. For the defect feature ambiguity that exists in underwater defects, the ConvNeXt Block module and Efficient-RepGFPN structure are introduced to enhance the feature extraction capability of the network, and the P2 detection layer is fused to enhance the detection capability of small-size defects such as cracks. The results show that the mean average precision (mAP0.5 and mAP0.5:0.95) of the improved algorithm are increased by 1.4% and 5.8%, and it exhibits good robustness and considerable detection effect for underwater defects.