A robotics and computer-aided procedure for defect evaluation in bridge inspection

被引：1
作者：
Francesco Potenza
Cecilia Rinaldi
Erika Ottaviano
Vincenzo Gattulli
机构：
[1] University of L’Aquila,Department of Civil, Construction
[2] University of Cassino and Southern Lazio,Architectural and Environmental Engineering
[3] Sapienza University of Rome,Department of Civil and Mechanical Engineering
来源：
Journal of Civil Structural Health Monitoring | 2020年 / 10卷
关键词：
Defect detection; Digital image processing; Computer-aided engineering; Railway bridge; Robotic inspection; Semi-automatic procedure; Infrastructure management;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Image processing may enhance condition assessment of bridge defects. In this perspective, we propose robotics and computer-aided procedure, which enables quantitative evaluation of defect extension with a specific storage organization, and performed by unmanned aerial vehicle (UAV). The methodology for defect evaluation uses color-based image processing. Data contained in digital images are taken on pre-classified structural elements. A campaign of UAV-based inspections has been performed to evidence the potentiality of the proposed procedure. Recurrent defects, occurring in infrastructure belonging to the Italian National railway system, allow evidencing the main features of the developed image-processing algorithm. The proposed process of damage detection and quantification is discussed with respect to both the level of automation that can be reached in each phase and the robustness of the used image processing adopted procedure.
引用
收藏
页码：471 / 484
页数：13
相关论文
共 99 条
[21]  
Rea P(2012)Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures Autom Constr 22 567-576
[22]  
Castelli G(2017)Deep learning-based crack damage detection using convolutional neural networks Comput Aided Civ Infrastruct Eng 32 361-378
[23]  
Ottaviano E(2017)Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types Comput Aided Civ Infrastruct Eng 33 731-747
[24]  
Rea P(2017)Structural damage detection with automatic feature-extraction through deep learning Comput Aided Civ Infrastruct Eng 32 1025-1046
[25]  
Ottaviano E(2019)Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images Struct Health Monit 18 653-674
[26]  
Vorotnikov S(2011)Vision-based hierarchical recognition for dismantling robot applied to interior renewal of buildings Comput Aided Civ Infrastruct Eng 26 336-355
[27]  
Ceccarelli M(2016)Concrete crack assessment using digital image processing and 3D scene reconstruction J Comput Civ Eng 30 1-19
[28]  
Kurenev P(2007)Simultaneous localization, mapping and moving object tracking Int J Robot Res 26 889-916
[29]  
Rea P(2018)Zernike-moment measurement of thin-crack width in images enables by dual-scale deep learning Comput Aided Civ Infrastruct Eng 34 367-384
[30]  
Ottaviano E(undefined)undefined undefined undefined undefined-undefined
12345678910