|Table of Contents|

Research of bridge defect automated localization based on UAV inspection images and BIM model(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2025年05期
Page:
135-144
Research Field:
隧道工程
Publishing date:

Info

Title:
Research of bridge defect automated localization based on UAV inspection images and BIM model
Author(s):
FU Chuanqing1;  WANG Shenxinyang1;  SHU Jiangpeng2; 3;  XU Qingkai4;  ZHANG Jinyong4;  JIANG You4;  XIE Yihua4;  XU Shengliang5
1.College of Civil Engineering,Zhejiang University of Technology,Hangzhou 310023,Zhejiang,China;2.College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,Zhejiang,China;3.Innovation Center of Yangtze River Delta,Zhejiang University,Jiaxing 314100,Zhejiang,China;4.China Railway 12th Bureau Group Urban Development and Construction Co.,Ltd, Suzhou 215011,Jiangsu,China;5.Ningbo Municipal Engineering Construction Group Ltd,Ningbo 315000,Zhejiang,China
Keywords:
bridge engineering;  bridge defect localization;  coordinate projection and mapping;  registration;  unmanned aerial vehicle;  BIM
PACS:
U446
DOI:
10.19815/j.jace.2024.08087
Abstract:
In order to realize the automatic real-time and accurate localization of local defect images in the 3D operation and maintenance model of bridges, a bridge defect automated localization method based on unmanned air vehicle(UAV)inspection images, global positioning system(GPS)coordinates and BIM models was proposed. Firstly, a coordinate projection and mapping method was proposed to map the defect image coordinates from GPS WGS-84 coordinate system to BIM model coordinate system. Then, a coordinate registration algorithm based on the BIM model was established to register and align the 2D defect image to the component unit of BIM model, and realized the linkage between the defect image and the BIM model. Finally, the algorithm was implemented by secondary development based on Autodesk Revit environment. The UAV was used to conduct the bridge defect acquisition and field test, and the accuracy and feasibility of the method were verified. The results show that based on the images taken by UAV and the contained GPS information, the proposed method can realize real-time automated accurate localization of bridge defects on BIM model. The overall localization mean absolute error is 19.50 cm, and the component localization accuracy rate is 98.8%, which basically meets the requirements of the bridge inspection. This research has enough feasibility and engineering application potential, and can provide accurate data basis and decision support for the long-term inspection and maintenance of bridges.

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Last Update: 2025-09-25