水力发电学报2025，Vol.44Issue(10)：73-84,12.DOI:10.11660/slfdxb.20251007
深度与迁移学习驱动的水下图像增强与裂缝量化研究
Underwater image enhancement and crack quantification driven by deep learning and transfer learning
摘要
Abstract
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.关键词
水下结构/水下裂缝/图像增强/裂缝量化/扩散模型Key words
underwater structures/underwater cracks/image enhancement/crack quantification/diffusion model分类
建筑与水利引用本文复制引用
林川,刘荣锋,苏燕,林威伟,胡泽林,杜哲镓..深度与迁移学习驱动的水下图像增强与裂缝量化研究[J].水力发电学报,2025,44(10):73-84,12.基金项目
国家自然科学基金项目(52109118) （52109118）
自然资源部丘陵山地地质灾害防治重点实验室(福建省地质灾害重点实验室)开放基金资助(FJKLGH2025K00) （福建省地质灾害重点实验室）
福州大学研究生教育教学改革项目(FYKC2023006) （FYKC2023006）
