Bridge Damage Detection and BIM Localization Methods Based on Image Recognition

Authors

LI Yu, Central Southern Safety and Environment Technology Institute Co ., Ltd., Wuhan, Hubei 430071, ChinaFollow
GUO Hongjun, Central Southern Safety and Environment Technology Institute Co ., Ltd., Wuhan, Hubei 430071, China

Abstract

To overcome insufficient information of 2D detection data in traditional bridge maintenance,which hinders precise decision-making,this study proposed an automated method based on deep learning and structure from motion (SfM ) technology,which realized the complete process from image recognition to the accurate mapping of damage information in a building information model (BIM ).First,the YOLOv 8 deep learning model was used to automatically detect bridge surface damage.Second,the SfM technology was applied for the local 3D reconstruction of the damage area and to determine the 3D coordinates of the damage.Then,the least squares method was used to align the local coordinate system with the coordinate system of the whole bridge 3D model to realize the accurate integration of the damage information.Finally,the case verification of actual bridges demonstrates that the positioning error of this method can be controlled within the range of 4‒15 mm,which effectively improves the efficiency and reliability of bridge detection and provides technical support for the automated maintenance of bridges based on BIM.

Publication Date

12-24-2025

DOI

10.14048/j.issn.1671-2579.2025.06.032

First Page

277

Last Page

286

Submission Date

December 2025

Recommended Citation

Yu, LI and Hongjun, GUO (2025) "Bridge Damage Detection and BIM Localization Methods Based on Image Recognition," Journal of China & Foreign Highway: Vol. 45: Iss. 6, Article 32.
DOI: 10.14048/j.issn.1671-2579.2025.06.032
Available at: https://zwgl1980.csust.edu.cn/journal/vol45/iss6/32

Reference

[1]LEHMAN M.The American society of civil engineers ’ report card on America ’s infrastructure [J]//Women in Infrastructure,2022:5-21.
[2]MCNALLY C,FERREIRA B,GORDON D LA.The Canadian infrastructure report card [M].Canada:The State of the Federation,2015.
[3]《中国公路学报 》编辑部.中国桥梁工程学术研究综述:2021 [J].中国公路学报,2021,34(2):1-97.Editorial Department of China Journal of Highway and Transport.Review on China ’s bridge engineering research:2021 [J].China Journal of Highway and Transport,2021,34(2):1-97.
[4]王凌波,王秋玲,朱钊,等.桥梁健康监测技术研究现状及展望 [J].中国公路学报,2021,34(12):25-45.WANG Lingbo,WANG Qiuling,ZHU Zhao,et al.Current status and prospects of research on bridge health monitoring technology [J].China Journal of Highway and Transport,2021,34(12):25-45.
[5]王智文,毛永嘉,丁幼亮.基于 Matlab-JAVA 融合的桥梁健康监测数据分析与预警平台 [J].中外公路,2023,43(4):137-140.WANG Zhiwen,MAO Yongjia,DING Youliang.Data analysis and early warning platform for bridge health monitoring based on Matlab-JAVA fusion [J].Journal of China & Foreign Highway,2023,43(4):137-140.
[6]郝常顺,刘保东,孙武鹏,等.大跨度铁路桥梁健康监测技术研究与应用综述 [J].铁道科学与工程学报,2025,22(4):1662 -1685.HAO Changshun,LIU Baodong,SUN Wupeng,et al.Review on research and application of health monitoring technology for long-span railway bridges [J].Journal of Railway Science and Engineering,2025,22(4):1662 -1685.
[7]苏子玥,杜文康,雷冬.基于机器视觉的公路桥梁变形测量及安全预警 [J].中外公路,2024,44(5):248-258.SU Ziyue,DU Wenkang,LEI Dong.Deformation measurement and safety warning of highway bridges based on machine vision [J].Journal of China & Foreign Highway,2024,44(5):248-258.
[8]王代君,周天图,傅俊敏,等.大型桥梁健康监测系统多源 异 构 数 据 集 成 研 究 [J].中 外 公 路,2023,43(2):100-106.WANG Daijun,ZHOU Tiantu,FU Junmin,et al.Research on multi-source and heterogeneous data integration of large bridge health monitoring system [J].Journal of China & Foreign Highway,2023,43(2):100-106.
[9]DANG N S,SHIM C S.BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges [J].IOP Conference Series:Earth and Environmental Science,2018,143:012032.
[10]DORAFSHAN S,THOMAS R J,MAGUIRE M.Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete[J].Construction and Building Materials,2018,186:1031 -1045.
[11]SACKS R,KEDAR A,BORRMANN A,et al.SeeBridge as next generation bridge inspection:Overview,information delivery manual and model view definition[J].Automation in Construction,2018,90:134-145.
[12]SCHÖNBERGER J L,FRAHM J M.Structure-from-motion revisited [C]//Las Vegas：2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ),2016.
[13]RAJADURAI R S,KANG S T.Automated vision-based crack detection on concrete surfaces using deep learning[J].Applied Sciences,2021,11(11):5229.
[14]NGUYEN S D,TRAN T S,TRAN V P,et al.Deep learning-based crack detection:A survey [J].International Journal of Pavement Research and Technology,2023,16(4):943-967.
[15]HAMISHEBAHAR Y,GUAN H,SO S,et al.A comprehensive review of deep learning-based crack detection approaches [J].Applied Sciences,2022,12(3):1374.
[16]HACIEFENDIOĞLU K,BAŞAĞA H B.Concrete road crack detection using deep learning-based faster R-CNN method [J].Iranian Journal of Science and Technology,Transactions of Civil Engineering,2022,46(2):1621 -1633.
[17]KRUACHOTTIKUL P,COOHAROJANANONE N,PHANOMCHOENG G,et al.Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure:A case of Thailand ’s department of highways[J].Journal of Civil Structural Health Monitoring,2021,11(4):949-965.
[18]DANG H V,RAZA M,NGUYEN T V,et al.Deep learning-based detection of structural damage using time-series data [J].Structure and Infrastructure Engineering,2021,17(11):1474 -1493.
[19]MATTHEWS B,PALERMO A,LOGAN T,et al.Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel [J].Construction and Building Materials,2024,438:137023.
[20]JIANG F Y,DONG S.Development of an integrated deep learning-based remaining strength assessment model for pipelines with random corrosion defects subjected to internal pressures [J].Marine Structures,2024,96:103637.
[21]WANG W D,CUI D P,AI C B,et al.Target-free recognition of cable vibration in complex backgrounds based on computer vision [J].Mechanical Systems and Signal Processing,2023,197:110392.
[22]CUI D P,WANG W D,PENG J,et al.Enhancing cable vibration measurement at long distances through super-resolution reconstruction and target foreground segmentation [J].International Journal of Structural Stability and Dynamics,2025,25(5):2550039.
[23]GIRSHICK R,DONAHUE J,DARRELL T,et al.Region-based convolutional networks for accurate object detection and segmentation [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(1):142-158.
[24]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137 -1149.
[25]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection [C]//Las Vegas,NV,USA:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ),2016:779-788.
[26]TERVEN J,CÓRDOVA-ESPARZA D M,ROMERO-GONZÁLEZ J A.A comprehensive review of YOLO architectures in computer vision:From YOLOv 1 to YOLOv 8 and YOLO-NAS [J].Machine Learning and Knowledge Extraction,2023,5(4):1680 -1716.