结构工程师2025，Vol.41Issue(4)：25-30,6.DOI:10.15935/j.cnki.jggcs.202504.0004
基于深度学习的水下混凝土结构表观缺陷智能化识别研究
Intelligent Identification of Apparent Defects in Underwater Structures Based on Deep Learning
摘要
Abstract
Apparent defects in underwater concrete structures are significantly challenged by complex environmental factors including water turbidity,variable lighting conditions,and flow velocity.These interferences lead to difficulties in defect localization and low recognition accuracy during underwater inspections.To address these limitations,this study proposes an intelligent recognition framework based on deep learning.The methodology integrates three key components:generation of a multi-scenario defect database replicating complex underwater environments;application of small-sample expansion and image enhancement algorithms for robust preprocessing;implementation of the YOLOv5 target detection algorithm for multi-category defect identification and localization.Experimental results demonstrate that the proposed approach achieves a mean average precision(mAP)of 83%and a recognition precision exceeding 83%.This framework effectively mitigates accuracy degradation caused by underwater environmental complexities and limited sample sizes,providing a reliable technical solution for automated structural health monitoring of submerged infrastructure.关键词
水下结构/表观缺陷/识别/YOLO算法/深度学习Key words
underwater structures/apparent defects/identification/YOLO algorithm/deep learning分类
建筑与水利引用本文复制引用
张东林,叶锡钧,陈德津,骆堪辉..基于深度学习的水下混凝土结构表观缺陷智能化识别研究[J].结构工程师,2025,41(4):25-30,6.基金项目
国家自然科学基金项目(51908136) （51908136）
