水利学报2025，Vol.56Issue(9)：1155-1165,11.DOI:10.13243/j.cnki.slxb.20240627
基于无人机图像和深度学习的水工混凝土结构三维重建和缺陷定量辨识方法
Three-dimensional reconstruction and defect quantitative identification method of hydraulic concrete structure based on UAV images and deep learning
摘要
Abstract
During the service life of concrete structures,they are prone to defects such as cracks,spalling,and cal-cium precipitation due to environmental erosion and multi-load coupling.Traditional manual inspection methods suffer from drawbacks,including low detection efficiency,high potential risks,and a high rate of misjudgment,making them difficult to implement in high-rise structural areas.To address these challenges,this paper proposes a method for 3D reconstruction and defect quantification of concrete structures using multi-view stereo vision and deep learning.Building on the U-Net3+model,the paper introduces the coordinate attention mechanism and a classifica-tion guidance module to enhance the model's ability to focus on minute defect areas and improve the accuracy of defect segmentation in complex,background-interfered environments.Recognizing that traditional machine vision detection based on 2D images struggles with defect localization and accurate size quantification,a 3D reconstruction method for concrete structures from drone images is developed.A visual 3D reconstruction framework is established to perform reverse engineering calculations and restore the imaging process.Defects identified in 2D images are inte-grated with the 3D real-world scene model,and a method for defect localization and quantification in concrete struc-tures using multi-view stereo vision is proposed.Concrete beams and a concrete high dam are used as engineering case studies to validate the accuracy of the proposed 3D reconstruction for concrete structures and the effectiveness of fine defect identification and localization.The results demonstrate that the method proposed in this paper enables pre-cise quantitative identification and spatial localization of defects in concrete structures,even under the complex back-ground interference of UAV-based aerial imagery.This approach significantly improves detection efficiency and pro-vides valuable technical support for long-term defect tracking and monitoring.关键词
水工混凝土结构/缺陷识别/无人机航拍/机器视觉/人工智能Key words
hydraulic concrete structure/defect identification/drone aerial photography/machine vision/artificial intelligence分类
建筑与水利引用本文复制引用
李扬涛,赵海涛,顾冲时,魏洋,包腾飞,向镇洋..基于无人机图像和深度学习的水工混凝土结构三维重建和缺陷定量辨识方法[J].水利学报,2025,56(9):1155-1165,11.基金项目
西藏自治区科技计划项目(XZ202501ZY0009) （XZ202501ZY0009）
国家杰出青年科学基金项目(52325803) （52325803）
国家自然科学基金项目(52378244) （52378244）
江苏省自然科学基金项目(BK20231293) （BK20231293）
江苏省重点研发计划项目(BE2020703) （BE2020703）
国家基金委区域创新联合基金重点项目(U22A20229) （U22A20229）
