A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch, Christian, Georgieva, Kristina, Kasireddy, Varun, Akinci, Burcu, Fieguth, Paul·2015·Advanced Engineering Informatics
DOI 10.1016/j.aei.2015.01.008

Cited by (1,000)

Items 201–300 of 1,000

  1. Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid
    Mianqing Zhong, Lichun Sui, Zhihua Wang, Dongming Hu · 2020 · Sensors
  2. Point Cloud–Based Concrete Surface Defect Semantic Segmentation
    Neshat Bolourian, Majid Nasrollahi, Fardin Bahreini, Amin Hammad · 2022 · Journal of Computing in Civil Engineering
  3. Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
    Andreas Sjölander, Valeria Belloni, Anders Ansell, Erik Nordström · 2023 · Sensors
  4. Corrosion induced stress field and cracking time of reinforced concrete with initial defects: Analytical modeling and experimental investigation
    Xiaogang Zhang, Minhui Li, Luping Tang, Shazim Ali Memon, Guojun Ma, Feng Xing, Hongfang Sun · 2017 · Corrosion Science
  5. Utilizing text recognition for the defects extraction in sewers CCTV inspection videos
    L. Minh Dang, Syed Ibrahim Hassan, Suhyeon Im, Irfan Mehmood, Hyeonjoon Moon · 2018 · Computers in Industry
  6. Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure
    Soojin Cho, Seunghee Park, Gichun Cha, Tae Keun Oh · 2018 · Applied Sciences
  7. Illumination Compensation Model with k -Means Algorithm for Detection of Pavement Surface Cracks with Shadow
    Ju Huyan, Wei Li, Susan Louise Tighe, Ranran Deng, Shuai Yan · 2019 · Journal of Computing in Civil Engineering
  8. Detecting healthy concrete surfaces
    Philipp Hüthwohl, Ioannis Brilakis · 2018 · Advanced Engineering Informatics
  9. Morphology-based defect detection in machined surfaces with circular tool-mark patterns
    Du‐Ming Tsai, Daniel E. Rivera Molina · 2018 · Measurement
  10. Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management
    Andres Angulo, Juan Antonio Vega-Fernández, Lina María Aguilar-Lobo, Shailendra Natraj, Gilberto Ochoa‐Ruiz · 2019 · Lecture notes in computer science
  11. Robust pixel-wise concrete crack segmentation and properties retrieval using image patches
    Yiqing Liu, Justin K. W. Yeoh · 2021 · Automation in Construction
  12. A review study on unmanned aerial vehicle and mobile robot technologies on damage inspection of reinforced concrete structures
    Jiehui Wang, Tamon Ueda · 2023 · Structural Concrete
  13. A graph‐based method for quantifying crack patterns on reinforced concrete shear walls
    Pedram Bazrafshan, Thinh On, S. Basereh, Pinar Okumus, A. Ebrahimkhanlou · 2023 · Comput. Aided Civ. Infrastructure Eng.
  14. Seed-Based Approach for Automated Crack Detection from Pavement Images
    Yuxiao Zhou, Feng Wang, Natarajan Meghanathan, Yaxiong Huang · 2016 · Transportation Research Record Journal of the Transportation Research Board
  15. Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters
    Nhat‐Duc Hoang · 2018 · Computational Intelligence and Neuroscience
  16. An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound
    Lin Chen, Haibei Xiong, Xiaohan Sang, Cheng Yuan, Xiuquan Li, Qingzhao Kong · 2021 · Structural Health Monitoring
  17. 3D reconstruction and segmentation system for pavement potholes based on improved structure-from-motion (SFM) and deep learning
    Niannian Wang, Jiaxiu Dong, Hongyuan Fang, Bin Li, Kejie Zhai, Duo Ma, Yibo Shen, Haobang Hu · 2023 · Construction and Building Materials
  18. A Brief Review and a New Graph-Based Image Analysis for Concrete Crack Quantification
    Mahsa Payab, Reza Abbasina, Mostafa Khanzadi · 2018 · Archives of Computational Methods in Engineering
  19. Augmenting a deep-learning algorithm with canal inspection knowledge for reliable water leak detection from multispectral satellite images
    Jiawei Chen, Pingbo Tang, Todd E. Rakstad, Patrick Michael, Xiran Zhou · 2020 · Advanced Engineering Informatics
  20. A grid‐based classification and box‐based detection fusion model for asphalt pavement crack
    Bao‐Luo Li, Qi Yu, Jian‐Sheng Fan, Yu-Fei Liu, Cheng Liu · 2022 · Computer-Aided Civil and Infrastructure Engineering
  21. Vision‐aided three‐dimensional damage quantification and finite element model geometric updating for reinforced concrete structures
    Qingzhao Kong, Jiaxuan Gu, Bing Xiong, Cheng Yuan · 2023 · Computer-Aided Civil and Infrastructure Engineering
  22. A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images
    Yusuke Fujita, Koji Shimada, Manabu Ichihara, Yoshihiko Hamamoto · 2017 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
  23. An Asphalt Damage Dataset and Detection System Based on RetinaNet for Road Conditions Assessment
    Gilberto Ochoa‐Ruiz, Andrés Alonso Angulo-Murillo, Alberto Ochoa, Lina María Aguilar-Lobo, Juan Antonio Vega-Fernández, Shailendra Natraj · 2020 · Applied Sciences
  24. Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure
    Quincy G. Alexander, Vedhus Hoskere, Yasutaka Narazaki, Andrew Maxwell, Billie F. Spencer · 2022 · AI in Civil Engineering
  25. Fast identification of concrete cracks using 1D deep learning and explainable artificial intelligence-based analysis
    Ganesh Kolappan Geetha, Sung‐Han Sim · 2022 · Automation in Construction
  26. Toward Automated Fabric Defect Detection: A Survey of Recent Computer Vision Approaches
    Rui Carrilho, Ehsan Yaghoubi, José Lindo, Kailash Hambarde, Hugo Pedro Martins Carriço Proença · 2024 · Electronics
  27. Crack Segmentation by Leveraging Multiple Frames of Varying Illumination
    Stephen J. Schmugge, Lance Rice, John Lindberg, Robert Grizziy, Chris Joffey, Min Chul Shin · 2017 · IEEE Winter Conference on Applications of Computer Vision
  28. A deep learning-based indoor acceptance system for assessment on flatness and verticality quality of concrete surfaces
    Dongsheng Li, Jiepeng Liu, Shenlin Hu, Guozhong Cheng, Yang Li, Yuxing Cao, Biqin Dong, Y. Frank Chen · 2022 · Journal of Building Engineering
  29. Enhancing pavement health assessment: An attention-based approach for accurate crack detection, measurement, and mapping
    Eshta Ranyal, Ayan Sadhu, Kamal Kishore Jain · 2024 · Expert Systems with Applications
  30. Vision-based monitoring of railway superstructure: A review
    Peyman Aela, Jiafu Cai, Guoqing Jing, Hung-Lin Chi · 2024 · Construction and Building Materials
  31. Detecting Structural Components of Building Engineering Based on Deep-Learning Method
    Xueliang Hou, Ying Zeng, Jingguo Xue · 2019 · Journal of Construction Engineering and Management
  32. Microcapsule‐Based Visualization Smart Sensors for Damage Detection: Principles and Applications
    Xu Zheng, Qing Wang, Yao Li, Jinjin Luan, Ning Wang · 2019 · Advanced Materials Technologies
  33. Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
    Alessio Martinelli, Monica Meocci, Marco Dolfi, Valentina Branzi, Simone Morosi, Fabrizio Argenti, Lorenzo Berzi, Tommaso Consumi · 2022 · Sensors
  34. An integrated underwater structural multi-defects automatic identification and quantification framework for hydraulic tunnel via machine vision and deep learning
    Yangtao Li, Tengfei Bao, Xianjun Huang, Ruijie Wang, Xiaosong Shu, Bo Xu, Jiuzhou Tu, Yuhang Zhou, Kang Zhang · 2022 · Structural Health Monitoring
  35. A novel transformer-based semantic segmentation framework for structural condition assessment
    Ruhua Wang, Yanda Shao, Qilin Li, Ling Li, Jun Li, Hong Hao · 2023 · Structural Health Monitoring
  36. Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches
    Stephen J. Schmugge, Lance Rice, N. Rich Nguyen, John Lindberg, Robert Grizzi, Chris Joffe, Min Chul Shin · 2016 · IEEE Winter Conference on Applications of Computer Vision
  37. Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis
    Nhat‐Duc Hoang, Quoc-Lam Nguyen, Xuan-Linh Tran · 2019 · Complexity
  38. Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety
    Yanyu Wang, Pin‐Chao Liao, Cheng Zhang, Yi Ren, Xinlu Sun, Pingbo Tang · 2019 · Advanced Engineering Informatics
  39. Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks
    Karim Tout, Anis Meguenani, Jean-Philippe Urban, Christophe Cudel · 2021 · The International Journal of Advanced Manufacturing Technology
  40. Simultaneous pixel-level concrete defect detection and grouping using a fully convolutional model
    Chaobo Zhang, C. C. Chang, Maziar Jamshidi · 2021 · Structural Health Monitoring
  41. A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning
    Nhat‐Duc Hoang, Thanh‐Canh Huynh, Xuan-Linh Tran, Van-Duc Tran · 2022 · Advances in Civil Engineering
  42. An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment
    Waqar Shahid Qureshi, Syed Ibrahim Hassan, Susan McKeever, David Power, Brian Mulry, Kieran Feighan, Dympna M. O’Sullivan · 2022 · Sensors
  43. Monitoring Asphalt Pavement Aging and Damage Conditions from Low-Altitude UAV Imagery Based on a CNN Approach
    Yifan Pan, Xiao Chen, Quan Sun, Xianfeng Zhang · 2021 · Canadian Journal of Remote Sensing
  44. Identification of concrete aggregates using K-means clustering and level set method
    Lei Chen, Wubin Shan, Peng Zheng Liu · 2021 · Structures
  45. A CNN model for predicting size of buried objects from GPR B-Scans
    Nairit Barkataki, Banty Tiru, Utpal Sarma · 2022 · Journal of Applied Geophysics
  46. Industrial machine tool component surface defect dataset
    Tobias Schlagenhauf, Magnus Landwehr · 2021 · Data in Brief
  47. Airport pavement responses obtained from wireless sensing network upon digital signal processing
    Zejiao Dong, Xianyong Ma, Xianzhi Shao · 2018 · International Journal of Pavement Engineering
  48. Damage detection with image processing: a comparative study
    Marianna Crognale, Melissa De Iuliis, Cecilia Rinaldi, Vincenzo Gattulli · 2023 · Earthquake Engineering and Engineering Vibration
  49. Topology-aware mamba for crack segmentation in structures
    Xin Zuo, Sheng Yu, Jifeng Shen, Yongwei Shan · 2024 · Automation in Construction
  50. Effect of concrete rheological properties on quality of formed surfaces cast with self-consolidating concrete and superworkable concrete
    Wael A. Megid, Kamal Henri Khayat · 2018 · Cement and Concrete Composites
  51. Defect detection of nuclear fuel assembly based on deep neural network
    Zhangpeng Guo, Zhiwang Wu, Sheng Liu, Xuan Ma, Chaoyi Wang, Dijiao Yan, Fenglei Niu · 2019 · Annals of Nuclear Energy
  52. ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection
    Fu‐Chen Chen, Mohammad R. Jahanshahi · 2020 · Machine Vision and Applications
  53. Deep learning–based roadway crack classification with heterogeneous image data fusion
    Shanglian Zhou, Wei Song · 2020 · Structural Health Monitoring
  54. Concrete roadway crack segmentation using encoder-decoder networks with range images
    Shanglian Zhou, Wei Song · 2020 · Automation in Construction
  55. A 3D reconstruction method for buildings based on monocular vision
    Boqiang Xu, Chao Liu · 2021 · Computer-Aided Civil and Infrastructure Engineering
  56. An advanced AI-based lightweight two-stage underwater structural damage detection model
    Xijun Ye, Kanhui Luo, Hanmin Wang, Yinghao Zhao, Jinwen Zhang, Airong Liu · 2024 · Advanced Engineering Informatics
  57. Automatic complex concrete crack detection and quantification based on point clouds and deep learning
    Shihong Chen, Gao Fan, Jun Li, Hong Hao · 2025 · Engineering Structures
  58. Bridge Column Maximum Drift Estimation via Computer Vision
    David Lattanzi, Gregory R. Miller, Marc O. Eberhard, Olafur Haraldsson · 2015 · Journal of Computing in Civil Engineering
  59. Considerations and Issues in the Utilization of Infrared Thermography for Concrete Bridge Inspection at Normal Driving Speeds
    Shuhei Hiasa, F. Necati Çatbaş, Masato Matsumoto, Koji Mitani · 2017 · Journal of Bridge Engineering
  60. Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN
    Mingzhu Wang, Jack C.P. Cheng · 2018 · Lecture notes in computer science
  61. Automated Pixel-Level Surface Crack Detection Using U-Net
    J. Ji, Li-Jun Wu, Zhi-Cong Chen, Jinling Yu, P. Lin, Shuying Cheng · 2018 · International Workshop on Multi-disciplinary Trends in Artificial Intelligence
  62. Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic
    Nhat‐Duc Hoang, Thanh‐Canh Huynh, Van-Duc Tran · 2021 · Complexity
  63. Personal Image Classifier Based Handy Pipe Defect Recognizer (HPD): Design and Test
    Ata Jahangir Moshayedi, Amir Sohail Khan, Shuxin Yang, S. Mohammadali Zanjani · 2022 · International Conference on Intelligent Computing and Signal Processing (ICSP)
  64. Deep Domain Adaptation for Pavement Crack Detection
    Huijun Liu, Yang Chun-hua, Ao Li, Sheng Huang, Xin Feng, Zhimin Ruan, Yongxin Ge · 2022 · IEEE Transactions on Intelligent Transportation Systems
  65. MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection
    Jing Zhang, Yuanyuan Cai, Dong Yang, Ye Yuan, Wen-Yu He, Yan-Jia Wang · 2023 · Construction and Building Materials
  66. Shifting research from defect detection to defect modeling in computer vision-based structural health monitoring
    Junjie Chen, Isabelle Chan, Ioannis Brilakis · 2024 · Automation in Construction
  67. Monitoring concrete bridge decks using infrared thermography with high speed vehicles
    Shuhei Hiasa, F. Necati Çatbaş, Masato Matsumoto, Koji Mitani · 2016 · Structural Monitoring and Maintenance
  68. A novel underwater dam crack detection and classification approach based on sonar images
    Pengfei Shi, Xinnan Fan, Jianjun Ni, Zubair Khan, Min Li · 2017 · PLoS ONE
  69. A novel pavement mean texture depth evaluation strategy based on three-dimensional pavement data filtered by a new filtering approach
    Liang Jia, Xingyu Gu, Yizheng Chen, Fujian Ni, Tianjie Zhang · 2020 · Measurement
  70. Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing
    Gang Li, Xueli Ren, Wenting Qiao, Biao Ma, Ying Li · 2020 · Structural Control & Health Monitoring
  71. Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN
    Felix Kortmann, Kevin Talits, Pascal Fassmeyer, Alexander Warnecke, Nicolas Meier, Jens Heger, Paul Drews, Burkhardt Funk · 2020 · IEEE International Conference on Big Data (BigData)
  72. BIM-Based Pavement Management Tool for Scheduling Urban Road Maintenance
    Cristina Oreto, Luigi Massotti, Salvatore Antonio Biancardo, Rosa Veropalumbo, Nunzio Viscione, Francesca Russo · 2021 · Infrastructures
  73. Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges
    Kao, Szu-Pyng, Chang, Yung-Chen, Wang, Feng-Liang · 2023 · Sensors
  74. FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning
    Yuan, Yachao, Yuan, Yali, Baker, Thar, Kolbe, Lutz Maria, Hogrefe, Dieter · 2021 · Future Generation Computer Systems
  75. Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures
    Keisuke Maeda, Sho Takahashi, Takahiro Ogawa, Miki Haseyama · 2019 · Computer-Aided Civil and Infrastructure Engineering
  76. Automated bughole detection and quality performance assessment of concrete using image processing and deep convolutional neural networks
    Wei Wei, Lieyun Ding, Hanbin Luo, Chen Li, Guowei Li · 2021 · Construction and Building Materials
  77. 3D visual inspection system framework for structural condition monitoring and analysis
    Maria Insa-Iglesias, Mark David Jenkins, Gordon Morison · 2021 · Automation in Construction
  78. Firm Size and Artificial Intelligence (AI)-Based Technology Adoption: The Role of Corporate Size in South Korean Construction Companies
    Seunguk Na, Seokjae Heo, Wonjun Choi, Sehee Han, C. Kim · 2023 · Buildings
  79. Fine-grained damage detection of cement concrete pavement based on UAV remote sensing image segmentation and stitching
    Shuangda Feng, Mingxing Gao, Xiaowei Jin, Ting Zhao, Feng Yang · 2023 · Measurement
  80. Deep Learning-Based Automated Detection of Cracks in Historical Masonry Structures
    Kemal Hacıefendioğlu, Ahmet Can Altunışık, Tuğba Abdioğlu · 2023 · Buildings
  81. OBJECT-BASED AND SUPERVISED DETECTION OF POTHOLES AND CRACKS FROM THE PAVEMENT IMAGES ACQUIRED BY UAV
    Yifan Pan, Xianfeng Zhang, M. Sun, Qing Zhao · 2017 · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
  82. Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System
    Andri Riid, Roland Lõuk, René Pihlak, Aleksei Tepljakov, Kristina Vassiljeva · 2019 · Applied Sciences
  83. Information fusion for automated post-disaster building damage evaluation using deep neural network
    Limao Zhang, Yue Pan · 2021 · Sustainable Cities and Society
  84. Tracking the Research on Ten Emerging Digital Technologies in the AECO Industry
    Yudan Dou, Tianxin Li, Long Li, Yuanxin Zhang, Zhongfu Li · 2023 · Journal of Construction Engineering and Management
  85. Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network
    Domen Tabernik, Matic Šuc, Danijel Skočaj · 2023 · Construction and Building Materials
  86. Automatic real-time crack detection using lightweight deep learning models
    Guoshao Su, Yuanzhuo Qin, Huajie Xu, Jin-Fu Liang · 2024 · Engineering Applications of Artificial Intelligence
  87. Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review
    Dayou Luo, Kejin Wang, Dongming Wang, Anuj Sharma, Wengui Li, In Ho Cho · 2025 · npj Materials Sustainability
  88. Computer Vision-Based Bridge Displacement Measurements Using Rotation-Invariant Image Processing Technique
    Byung-Wan Jo, Yun‐Sung Lee, Jun Ho Jo, Rana Muhammad Asad Khan · 2018 · Sustainability
  89. Real‐Time Road Crack Mapping Using an Optimized Convolutional Neural Network
    M-Mahdi Naddaf-Sh, Seyedsaeid Hosseini, Jing Zhang, Nicholas Andres Brake, Hassan Zargarzadeh · 2019 · Complexity
  90. Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification
    Ganesh Kolappan Geetha, Hyun-Jung Yang, Sung‐Han Sim · 2023 · Sensors
  91. Machine Vision-Based Monitoring Methodology for the Fatigue Cracks in U-Rib-to-Deck Weld Seams
    Dalei Wang, Yiqing Dong, Yue Pan, Rujin Ma · 2020 · IEEE Access
  92. Defect Detection of Subway Tunnels Using Advanced U-Net Network
    An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2022 · Sensors
  93. Using supervised machine learning algorithms in pavement degradation monitoring
    Amir Shtayat, Sara Moridpour, Berthold Best, Mohammad Abuhassan · 2022 · International Journal of Transportation Science and Technology
  94. AI-powered shotcrete robot for enhancing structural integrity using ultra-high performance concrete and visual recognition
    Tzu-Hsuan Lin, Chien-Ta Chang, Bo-Hong Yang, Chung‐Chan Hung, Kuo-Wei Wen · 2023 · Automation in Construction
  95. A lightweight deep learning network based on knowledge distillation for applications of efficient crack segmentation on embedded devices
    Jun Chen, Ye Liu, Jia-ao Hou · 2023 · Structural Health Monitoring
  96. Fractional derivative Burgers models describing dynamic viscoelastic properties of asphalt binders
    Xinzhou Li, Aimin Sha, Wenxiu Jiao, Ruimeng Song, Yangsen Cao, Chao Li, Zhuangzhuang Liu · 2023 · Construction and Building Materials
  97. Intelligent real-time quality control for 3D-printed concrete with near-nozzle secondary mixing
    Hanghua Zhang, Yanke Tan, Lucen Hao, Shipeng Zhang, Jianzhuang Xiao, Chi Sun Poon · 2024 · Automation in Construction
  98. Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview
    Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui · 2025 · Deep Underground Science and Engineering
  99. Fully Automated Road Defect Detection Using Street View Images
    David Abou Chacra, John Zelek · 2017 · Canadian Conference on Computer and Robot Vision
  100. Real-Time Stereo Vision for Road Surface 3-D Reconstruction
    Rui Fan, Yanan Liu, Xingrui Yang, Mohammud Junaid Bocus, Naim Dahnoun, Scott Tancock · 2018 · IEEE International Conference on Imaging Systems and Techniques (IST)