A review of computer vision–based structural health monitoring at local and global levels

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to t...

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[58]  Dashan Zhang,et al.  A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms , 2016, Sensors.

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[68]  Shuhei Hiasa,et al.  Practical identification of favorable time windows for infrared thermography for concrete bridge evaluation , 2015 .

[69]  Pedro Arias,et al.  Exploitation of Geometric Data provided by Laser Scanning to Create FEM Structural Models of Bridges , 2016 .

[70]  Namgyu Kim,et al.  Deep learning–based autonomous concrete crack evaluation through hybrid image scanning , 2019, Structural Health Monitoring.

[71]  Robert J. Thomas,et al.  Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete , 2018, Construction and Building Materials.

[72]  Gwolong Lai,et al.  Application of digital photogrammetry techniques in identifying the mode shape ratios of stay cables with multiple camcorders , 2015 .

[73]  Fubin Wang,et al.  A High-Speed Target-Free Vision-Based Sensor for Bus Rapid Transit Viaduct Vibration Measurements Using CMT and ORB Algorithms , 2017, Sensors.

[74]  Kyoung-Chan Lee,et al.  Long-term displacement measurement of full-scale bridges using camera ego-motion compensation , 2020 .

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[77]  Shuhei Hiasa,et al.  Effect of Defect Size on Subsurface Defect Detectability and Defect Depth Estimation for Concrete Structures by Infrared Thermography , 2017 .

[78]  Junhua Sun,et al.  A Fast Bolt-Loosening Detection Method of Running Train’s Key Components Based on Binocular Vision , 2019, IEEE Access.

[79]  Luh-Maan Chang,et al.  Automated steel bridge coating rust defect recognition method based on color and texture feature , 2013 .

[80]  Emanuele Zappa,et al.  Vision-based estimation of vertical dynamic loading induced by jumping and bobbing crowds on civil structures , 2012 .

[81]  Lijun Wu,et al.  Dynamic testing of a laboratory model via vision-based sensing , 2014 .

[82]  Xianyu Jin,et al.  Time-varying relative displacement field on the surface of concrete cover caused by reinforcement corrosion based on DIC measurement , 2018 .

[83]  Fu Q. Zhong,et al.  Three-dimensional digital image correlation with improved efficiency and accuracy , 2018, Measurement.

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[86]  Chuan-Zhi Dong,et al.  Marker-free monitoring of the grandstand structures and modal identification using computer vision methods , 2018, Structural Health Monitoring.

[87]  F. Necati Catbas,et al.  Swaying displacement measurement for structural monitoring using computer vision and an unmanned aerial vehicle , 2020 .

[88]  Ashutosh Bagchi,et al.  Image-based retrieval of concrete crack properties for bridge inspection , 2014 .

[89]  Sung-Han Sim,et al.  Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges , 2017, Sensors.

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[99]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[100]  Mani Golparvar-Fard,et al.  Target‐free approach for vision‐based structural system identification using consumer‐grade cameras , 2016 .

[101]  Yoshihiko Hamamoto,et al.  A robust automatic crack detection method from noisy concrete surfaces , 2011, Machine Vision and Applications.

[102]  Chang-Soo Han,et al.  Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .

[103]  Kaoshan Dai,et al.  An Estimation of Pedestrian Action on Footbridges Using Computer Vision Approaches , 2019, Front. Built Environ..

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[105]  Danhui Dan,et al.  Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision , 2019, Measurement.

[106]  Myra Lydon,et al.  Development and Field Testing of a Time-Synchronized System for Multi-Point Displacement Calculation Using Low-Cost Wireless Vision-Based Sensors , 2018, IEEE Sensors Journal.

[107]  Ali Khaloo,et al.  Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam , 2018, Front. Built Environ..

[108]  B. F. Spencer,et al.  Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles , 2017, Sensors.

[109]  Billie F. Spencer,et al.  Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring , 2019, Engineering.

[110]  Yan Xu,et al.  Accurate Deformation Monitoring on Bridge Structures Using a Cost-Effective Sensing System Combined with a Camera and Accelerometers: Case Study , 2019, Journal of Bridge Engineering.

[111]  Javad Baqersad,et al.  An optical-based technique to obtain operating deflection shapes of structures with complex geometries , 2019, Mechanical Systems and Signal Processing.

[112]  Ivan Roselli,et al.  Motion Magnification Analysis for structural monitoring of ancient constructions , 2018, Measurement.

[113]  Ignacio Parra,et al.  Adaptive Road Crack Detection System by Pavement Classification , 2011, Sensors.

[114]  Shirley J. Dyke,et al.  Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure , 2019 .

[115]  Shuhei Hiasa,et al.  Infrared thermography for civil structural assessment: demonstrations with laboratory and field studies , 2016 .

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[117]  Y. F. Xu.  Photogrammetry-based structural damage detection by tracking a visible laser line , 2019, Structural Health Monitoring.

[118]  Liming Zhou,et al.  A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision , 2019, Comput. Aided Civ. Infrastructure Eng..

[119]  Rih-Teng Wu,et al.  Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification , 2019, Journal of Engineering Mechanics.

[120]  Jian Zhang,et al.  Pixel‐level crack delineation in images with convolutional feature fusion , 2018, Structural Control and Health Monitoring.

[121]  Xiao Liang,et al.  Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization , 2018, Comput. Aided Civ. Infrastructure Eng..

[122]  Gang Li,et al.  Long-distance precision inspection method for bridge cracks with image processing , 2014 .

[123]  Peter Avitabile,et al.  Large-area photogrammetry based testing of wind turbine blades , 2017 .

[124]  Myra Lydon,et al.  Development and field testing of a vision-based displacement system using a low cost wireless action camera , 2019, Mechanical Systems and Signal Processing.

[125]  F. Necati Catbas,et al.  Structural Identification Using Computer Vision–Based Bridge Health Monitoring , 2018 .

[126]  Paulo B. Lourenço,et al.  Application of digital image correlation in investigating the bond between FRP and masonry , 2013 .

[127]  Emanuele Zappa,et al.  Vibration Monitoring of Multiple Bridge Points by Means of a Unique Vision-Based Measuring System , 2014 .

[128]  Ali Khaloo,et al.  Damage Detection and Finite-Element Model Updating of Structural Components through Point Cloud Analysis , 2018, Journal of Aerospace Engineering.

[129]  Luis Felipe-Sesé,et al.  Damage methodology approach on a composite panel based on a combination of Fringe Projection and 2D Digital Image Correlation , 2018 .

[130]  He Zhang,et al.  Crack Propagation and Fracture Process Zone (FPZ) of Wood in the Longitudinal Direction Determined Using Digital Image Correlation (DIC) Technique , 2019, Remote. Sens..

[131]  Ling Shao,et al.  Measuring human-induced vibrations of civil engineering structures via vision-based motion tracking , 2016 .

[132]  F. Necati Catbas,et al.  Completely contactless structural health monitoring of real‐life structures using cameras and computer vision , 2017 .

[133]  Chung-Ming Yang,et al.  Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis , 2015, Adv. Eng. Softw..

[134]  Chuan-Zhi Dong,et al.  Structural displacement monitoring using deep learning-based full field optical flow methods , 2020, Structure and Infrastructure Engineering.

[135]  Hao Sun,et al.  Camera-Based Vibration Measurement of the World War I Memorial Bridge in Portsmouth, New Hampshire , 2018, Journal of Structural Engineering.

[136]  Shivprakash Iyer,et al.  Segmentation of Pipe Images for Crack Detection in Buried Sewers , 2006, Comput. Aided Civ. Infrastructure Eng..

[137]  Maria Q. Feng,et al.  Vision‐based multipoint displacement measurement for structural health monitoring , 2016 .

[138]  Moncef L. Nehdi,et al.  Infrared thermography model for automated detection of delamination in RC bridge decks , 2018 .

[139]  Chul Min Yeum,et al.  Vision‐Based Automated Crack Detection for Bridge Inspection , 2015, Comput. Aided Civ. Infrastructure Eng..

[140]  Yu Zhao,et al.  Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms , 2020 .

[141]  Xiaodong Ji,et al.  Vision-based measurements of deformations and cracks for RC structure tests , 2020 .

[142]  Quoc-Lam Nguyen,et al.  Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network , 2018, Automation in Construction.

[143]  X. W. Ye,et al.  Force monitoring of steel cables using vision-based sensing technology: methodology and experimental verification , 2016 .

[144]  Takashi Matsumoto,et al.  Development of an Automatic Detector of Cracks in Concrete Using Machine Learning , 2017 .

[145]  Koichi Hishida,et al.  Flow structure of microbubble-laden turbulent channel flow measured by PIV combined with the shadow image technique , 2005 .

[146]  Kuo-Wei Liao,et al.  Detection of rust defects on steel bridge coatings via digital image recognition , 2016 .

[147]  Bing Pan,et al.  Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation , 2016 .

[148]  Jian Zhang,et al.  Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry , 2019, Comput. Aided Civ. Infrastructure Eng..

[149]  Kristin J. Dana,et al.  Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.

[150]  Jian Li,et al.  Image Registration-Based Bolt Loosening Detection of Steel Joints , 2018, Sensors.

[151]  Onur Avci,et al.  A novel video-vibration monitoring system for walking pattern identification on floors , 2020, Adv. Eng. Softw..

[152]  Michael J. Black,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.

[153]  Siddhartha Kumar Khaitan,et al.  Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection , 2017 .

[154]  Xiaochun Luo,et al.  Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network , 2018, Comput. Aided Civ. Infrastructure Eng..

[155]  Rui Calçada,et al.  Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system , 2014 .

[156]  Xi Chu,et al.  A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm , 2020 .

[157]  Ulas Bagci,et al.  Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems , 2018, ArXiv.

[158]  Shanshan Yu,et al.  Vision-based structural scaling factor and flexibility identification through mobile impact testing , 2019, Mechanical Systems and Signal Processing.

[159]  Gangbing Song,et al.  Design of a New Vision-Based Method for the Bolts Looseness Detection in Flange Connections , 2020, IEEE Transactions on Industrial Electronics.

[160]  Young-Jin Cha,et al.  Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm , 2019 .

[161]  Eugene J. O'Brien,et al.  Contactless Bridge Weigh-in-Motion , 2016 .

[162]  Sangwook Lee,et al.  Automated recognition of surface defects using digital color image processing , 2006 .

[163]  Shudong Chen,et al.  Accurate Measurement of Characteristic Response for Unexploded Ordnance With Transient Electromagnetic System , 2020, IEEE Transactions on Instrumentation and Measurement.

[164]  Chih-Chen Chang,et al.  Flexible Videogrammetric Technique for Three-Dimensional Structural Vibration Measurement , 2007 .

[165]  Billie F. Spencer,et al.  Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction , 2016, J. Comput. Civ. Eng..

[166]  Wilson Ricardo Leal da Silva,et al.  Concrete Cracks Detection Based on Deep Learning Image Classification , 2018, Proceedings.

[167]  Sez Atamturktur,et al.  Novel vibration-based technique for detecting water pipeline leakage , 2017 .

[168]  Soojin Cho,et al.  Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique , 2018, Sensors.

[169]  Luh-Maan Chang,et al.  Support-vector-machine-based method for automated steel bridge rust assessment , 2012 .

[170]  Shuji Hashimoto,et al.  Fast crack detection method for large-size concrete surface images using percolation-based image processing , 2010, Machine Vision and Applications.

[171]  Moncef L. Nehdi,et al.  Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography , 2017 .

[172]  Jian Li,et al.  Vision‐Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking , 2018, Comput. Aided Civ. Infrastructure Eng..

[173]  Shuhei Hiasa,et al.  Investigation of effective utilization of infrared thermography (IRT) through advanced finite element modeling , 2017 .

[174]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[175]  Chuan-Zhi Dong,et al.  A non-target structural displacement measurement method using advanced feature matching strategy , 2019, Advances in Structural Engineering.

[176]  James M. W. Brownjohn,et al.  Review of machine-vision based methodologies for displacement measurement in civil structures , 2018 .

[177]  Maria Q. Feng,et al.  Cable tension force estimate using novel noncontact vision-based sensor , 2017 .

[178]  Chuan-Zhi Dong,et al.  A computer vision approach for the load time history estimation of lively individuals and crowds , 2018 .

[179]  Paul Fieguth,et al.  Automated detection of cracks in buried concrete pipe images , 2006 .

[180]  Debasis Deb,et al.  Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique , 2016 .

[181]  Mustafa Gul,et al.  A cost effective solution for pavement crack inspection using cameras and deep neural networks , 2020 .

[182]  Charles R. Farrar,et al.  Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements , 2017 .

[183]  Jie Gao,et al.  Recognition of asphalt pavement crack length using deep convolutional neural networks , 2018 .

[184]  Kelvin C. P. Wang,et al.  Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V , 2020, IEEE Transactions on Intelligent Transportation Systems.

[185]  Luh-Maan Chang,et al.  Human-visual-perception-like intensity recognition for color rust images based on artificial neural network , 2018, Automation in Construction.

[186]  Shuhei Hiasa,et al.  Experimental and numerical studies for suitable infrared thermography implementation on concrete bridge decks , 2018, Measurement.

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