Acquiring high-quality underwater crack images and achieving efficient identification and quantification are crucial for enhancing dam inspection efficiency. To address the challenges associated with underwater image degradation and crack quantification

this study develops a deep learning and transfer learning-based method for underwater image enhancement and crack analysis. We construct a new platform for underwater imaging and data acquisition

and develop a conditional diffusion model using public marine image datasets as prior knowledge for cross-domain multi-source enhancement. Crack detection is performed using YOLOv12

followed by morphological operations for feature quantification. Experimental results demonstrate our method significantly outperforms conventional approaches in terms of visual quality

no-reference metrics

and pixel allocation. The integrated detection model improves accuracy while reducing missed detections

and the quantification method extracts crack parameters effectively. The enhancement-identification-quantification closed-loop framework developed in this study is an effective technical solution to intelligent underwater inspections.