Autonomous concrete crack detection using deep fully convolutional neural network

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

A Threshold Selection Method from Gray-Level Histograms

Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach

Yilmaz, M., Doğan, G., Arslan, M. H., & İlki̇, A. (2024) · Journal of Earthquake Engineering · doi:10.1080/13632469.2024.2302033 · Cited by 36

Deep neural networks for crack detection inside structures

Moreh, F., Lyu, H., Rizvi, Z. H., & Wuttke, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-54494-y · Cited by 43

Research on a self-powered rolling bearing fault diagnosis method with a piezoelectric generator for self-sensing

Shi, R., Yan, Z., Fang, S., Qiao, Z., Xiao, S., Lei, J., Wang, Z., Xu, B., & Lai, Z. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124206 · Cited by 37

An Automated Inspection Method for the Steel Box Girder Bottom of Long-Span Bridges Based on Deep Learning

Wang, D., Zhang, Y., Pan, Y., Peng, B., Liu, H., & Ma, R. (2020) · IEEE Access · doi:10.1109/access.2020.2994275 · Cited by 34

Mask Gradient Response-Based Threshold Segmentation for Surface Defect Detection of Milled Aluminum Ingot

Liang, Y., Xu, K., & Zhou, P. (2020) · Sensors · doi:10.3390/s20164519 · Cited by 37

Deep Learning Approach-Based Network Intrusion Detection System for Fog-Assisted IoT

Sahar, N., Mishra, R., & Kalam, S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-8377-3_4 · Cited by 36

Earthquake Crack Detection From Aerial Images Using a Deformable Convolutional Neural Network

Yu, D., Ji, S., Li, X., Yuan, Z., & Shen, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3183157 · Cited by 36

Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network

Hammouch, W., Chouiekh, C., Khaissidi, G., & Mrabti, M. (2022) · Infrastructures · doi:10.3390/infrastructures7110152 · Cited by 36

High-accuracy rebar position detection using deep learning–based frequency-difference electrical resistance tomography

Jeon, D., Kim, M. K., Jeong, Y., Oh, J. E., Moon, J., Kim, D. J., & Yoon, S. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.104116 · Cited by 37

A hybrid method for pavement crack width measurement

Ong, J. C. H., Ismadi, M.-Z. P., & Wang, X. (2022) · Measurement · doi:10.1016/j.measurement.2022.111260 · Cited by 35

Embankment crack detection in UAV images based on efficient channel attention U2Net

Cheng, H., Li, Y., Li, H., & Hu, Q. (2023) · Structures · doi:10.1016/j.istruc.2023.02.010 · Cited by 35

Predicting micromechanical properties of cement paste from backscattered electron (BSE) images by computer vision

Liang, M., He, S., Gan, Y., Zhang, H., Chang, Z., Schlangen, E., & Šavija, B. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.111905 · Cited by 35

Enriched multi-scale cascade pyramid features and guided context attention network for industrial surface defect detection

Shao, L., Zhang, E., Duan, J., & Ma, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106369 · Cited by 36

Influence of mesoscopic pore characteristics on the splitting-tensile strength of cellular concrete through deep-learning based image segmentation

Fang, X., Wang, C., Li, H., Wang, X., Zhang, S., Luo, X., & He, J. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.125335 · Cited by 35

SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds

Ma, L., & Li, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102836 · Cited by 35

Crack recognition on concrete structures based on machine crafted and hand crafted features

Priyadharshini, R. A., Arivazhagan, S., & Arun, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120447 · Cited by 35

Deep learning paradigm for prediction of stress distribution in damaged structural components with stress concentrations

Bolandi, H., Li, X., Salem, T., Boddeti, V. N., & Lajnef, N. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103240 · Cited by 36

Intelligent analysis method of dam material gradation for asphalt-core rock-fill dam based on enhanced Cascade Mask R-CNN and GCNet

Zhang, Y., Ma, Y., Li, Y., & Wen, L. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102001 · Cited by 35

Toward Vision-Based Concrete Crack Detection: Automatic Simulation of Real-World Cracks

Dinh, T. H., Anh, V. T. T., Nguyen, T., Le, C. H., Trung, N. L., Duc, N. D., & Lin, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3328076 · Cited by 34

A real-time multi-defect automatic identification framework for concrete dams via improved YOLOv5 and knowledge distillation

Li, Y., & Bao, T. (2023) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00684-7 · Cited by 34

Computer Vision Applications In Construction And Asset Management Phases: A Literature Review

Jiang, Z., & Messner, J. (2023) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2023.009 · Cited by 39

Spatial attention-based dual stream transformer for concrete defect identification

Yadav, D. P., Chauhan, S., Kada, B., & Kumar, A. (2023) · Measurement · doi:10.1016/j.measurement.2023.113137 · Cited by 33

A hierarchical federated learning framework for collaborative quality defect inspection in construction

Wu, H., Li, H., Chi, H.-L., Kou, W.-B., Wu, Y., & Wang, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108218 · Cited by 33

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

Forest, F., Porta, H., Tuia, D., & Fink, O. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105497 · Cited by 37

CBRFormer: rendering technology-based transformer for refinement segmentation of bridge crack images

Chu, H.-H., Gai, J., Chen, W., & Ma, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103868 · Cited by 33

LE–MSFE–DDNet: a defect detection network based on low-light enhancement and multi-scale feature extraction

Hu, W., Wang, T., Wang, Y., Chen, Z., & Huang, G. (2021) · The Visual Computer · doi:10.1007/s00371-021-02210-6 · Cited by 35

UAV-Based Bridge Inspection via Transfer Learning

Aliyari, M., Droguett, E. L., & Ayele, Y. Z. (2021) · Sustainability · doi:10.3390/su132011359 · Cited by 33

Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics

Perry, B. J., Guo, Y., & Mahmoud, H. N. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104022 · Cited by 33

Modeling nonlinear flutter behavior of long‐span bridges using knowledge‐enhanced long short‐term memory network

Li, T., & Wu, T. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13000 · Cited by 33

High-speed acquisition and intelligent tunnel surface defects recognition

Wang, Y., Liao, W., Dong, A., Xu, L., Zhu, L., Shi, H., & Yu, Z. (2023) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2023.105572 · Cited by 32

A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks

Zhang, Y., & Zhang, L. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13231 · Cited by 34

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification

Bhattacharya, G., Mandal, B., & Puhan, N. B. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3028008 · Cited by 32

A Real-Time Detection Method for Concrete Surface Cracks Based on Improved YOLOv4

Yao, G., Sun, Y., Wong, M., & Lv, X. (2021) · Symmetry · doi:10.3390/sym13091716 · Cited by 32

Two-step deep learning approach for pavement crack damage detection and segmentation

Jiang, Y., Pang, D., Li, C., Yu, Y., & Cao, Y. (2022) · International Journal of Pavement Engineering · doi:10.1080/10298436.2022.2065488 · Cited by 32

Automatic damage segmentation in pavement videos by fusing similar feature extraction siamese network (SFE-SNet) and pavement damage segmentation capsule network (PDS-CapsNet)

Dong, J., Wang, N., Fang, H., Wu, R., Zheng, C., Ma, D., & Hu, H. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104537 · Cited by 30

Image segmentation of underfloor scenes using a mask regions convolutional neural network with two-stage transfer learning

Atkinson, G. A., Zhang, W., Hansen, M., Holloway, M., & Napier, A. A. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103118 · Cited by 32

A novel approach for pavement distress detection and quantification using RGB-D camera and deep learning algorithm

Lin, W., Li, X., Han, H., Yu, Q., & Cho, Y. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.133593 · Cited by 33

Deep learning-based corrosion inspection of long-span bridges with BIM integration

Hattori, K., Oki, K., Sugita, A., Sugiyama, T., & Chun, P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35308 · Cited by 31

Deep learning for automated detection and classification of crack severity level in concrete structures

Shi, T., & Luo, H. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.140793 · Cited by 31

Deep learning-based morphology classification of activated sludge flocs in wastewater treatment plants

Satoh, H., Kashimoto, Y., Takahashi, N., & Tsujimura, T. (2020) · Environmental Science Water Research & Technology · doi:10.1039/d0ew00908c · Cited by 33

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Abdelkader, E. M. (2021) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-01-2021-0010 · Cited by 31

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Xiu-ying, M. (2021) · Scientific Programming · doi:10.1155/2021/3137083 · Cited by 31

Application of deep convolutional neural networks for automated and rapid identification and computation of crack statistics of thin cracks in strain hardening cementitious composites (SHCCs)

Das, A. K., Leung, C. K. Y., & Wan, K. T. (2021) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2021.104159 · Cited by 37

Artificial neural networks applications in construction and building engineering (1991–2021): Science mapping and visualization

Marzouk, M. M., Elhakeem, A., & Adel, K. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111174 · Cited by 32

Structural performance assessment of RC flexural members through crack-based fibre beam-column model updating

Jing-lin, X., Nie, X., Liu, Y., & Fan, J. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107029 · Cited by 30

Dynamic & norm-based weights to normalize imbalance in back-propagated gradients of physics-informed neural networks

Deguchi, S., & Asai, M. (2023) · Journal of Physics Communications · doi:10.1088/2399-6528/ace416 · Cited by 30

Mobile-DenseNet: Detection of building concrete surface cracks using a new fusion technique based on deep learning

Akgül, İ. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21097 · Cited by 30

Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection

Zhang, C., Yin, Z., & Qin, R. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105292 · Cited by 30

An average pooling designed Transformer for robust crack segmentation

Chen, Z., Shamsabadi, E. A., Jiang, S., Shen, L., & Dias‐da‐Costa, D. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105367 · Cited by 34

A GAN-based method for diagnosing bodywork spot welding defects in response to small sample condition

Chen, G., Buyun, S., Fu, G., Chen, X., & Guangde, Z. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111544 · Cited by 30

Pavement Crack Detection Based on the Improved Swin-Unet Model

Chen, S., Feng, Z., Xiao, G., Chen, X., Gao, C., Zhao, M., & Yu, H. (2024) · Buildings · doi:10.3390/buildings14051442 · Cited by 30

A novel transfer learning model for the real-time concrete crack detection

Qingyi, W., & Chen, B. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112313 · Cited by 30

Comparative analysis of deep learning models for crack detection in buildings

Krishnan, S., Karuppan, M. K. N., Khadidos, A. O., Khadidos, A. O., Selvarajan, S., Tandon, S., & Balusamy, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-85983-3 · Cited by 30

A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities

Pan, X., Yang, T. Y., Li, J., Ventura, C. E., Málaga‐Chuquitaype, C., Li, C., Su, R. K. L., & Brzev, S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10279-8 · Cited by 36

A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks

Słoński, M. (2019) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.24423/cames.267 · Cited by 33

Imaging techniques for defect detection of fiber reinforced polymer‐bonded civil infrastructures

Qiu, Q. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2555 · Cited by 30

Image‐based road crack risk‐informed assessment using a convolutional neural network and an unmanned aerial vehicle

Ji, A., Xue, X., Wang, Y., Luo, X., & Wang, L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2749 · Cited by 30

Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism

Chew, A. W. Z., Ji, A., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103995 · Cited by 30

An algorithm for structural health monitoring by digital image correlation: Proof of concept and case study

Curt, J., Capaldo, M., François Hild, A., & Roux, S. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106842 · Cited by 30

TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection

Ju, X., Zhao, X., & Qian, S. (2022) · Mathematics · doi:10.3390/math10132354 · Cited by 31

Geometry-informed deep learning-based structural component segmentation of post-earthquake buildings

Wang, Y., Jing, X., Chen, W., Li, H., Xu, Y., & Zhang, Q. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.110028 · Cited by 30

End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

Bai, Y., Sezen, H., & Yilmaz, A. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9413041 · Cited by 32

Defect detection algorithm for magnetic particle inspection of aviation ferromagnetic parts based on improved DeepLabv3+

Zeng, L., Zhang, S., Wang, P., Li, Z., Hu, Y., & Xie, T. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acb9ae · Cited by 29

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map

Yamaguchi, T., & Mizutani, T. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13132 · Cited by 29

Efficient hybrid ensembles of CNNs and transfer learning models for bridge deck image-based crack detection

Mayya, A. M., Alkayem, N. F., Shen, L., Zhang, X., Fu, R., Wang, Q., & Cao, M. (2024) · Structures · doi:10.1016/j.istruc.2024.106538 · Cited by 29

Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review

Rakoczy, A. M., Ribeiro, D. R. F., Hoskere, V., Narazaki, Y., Olaszek, P., Karwowski, W., Cabral, R., Guo, Y., Futai, M. M., Milillo, P., et al. (2024) · Structural Engineering International · doi:10.1080/10168664.2024.2368220 · Cited by 31

Scale–Space Data Augmentation for Deep Transfer Learning of Crack Damage from Small Sized Datasets

Tang, S., & Chen, Z. (2020) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-020-00715-z · Cited by 30

An adaptive learning approach to determine and update crack sizes from strain relaxation data for welded plate joints

Feng, L., & Qian, X. (2021) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2021.108165 · Cited by 29

Bounding-box object augmentation with random transformations for automated defect detection in residential building façades

Lee, K., Lee, S., & Kim, H. Y. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104138 · Cited by 29

Surface Damage Identification for Heritage Site Protection: A Mobile Crowd-sensing Solution Based on Deep Learning

Meklati, S., Boussora, K., Abdi, M. E. H., & Berrani, S.-A. (2022) · Journal on Computing and Cultural Heritage · doi:10.1145/3569093 · Cited by 28

Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+

Hadinata, P. N., Simanta, D., Eddy, L., & Nagai, K. (2023) · Applied Sciences · doi:10.3390/app13042398 · Cited by 29

Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

Hamidia, M., Afzali, M., Jamshidian, S., & Safi, M. (2023) · Structural Concrete · doi:10.1002/suco.202200351 · Cited by 29

A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference

Deng, J., Lü, Y., & Lee, V. C. (2023) · Measurement · doi:10.1016/j.measurement.2023.112892 · Cited by 29

Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

Jia, L., Zhang, Q., & Gu, X. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105214 · Cited by 29

Deep learning-assisted automatic quality assessment of concrete surfaces with cracks and bugholes

Liu, J., Yang, Z., Qi, H., Jiao, T., Li, D., Wu, Z., Zheng, N., & Xu, S. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102577 · Cited by 28

Deep learning in crack detection: A comprehensive scientometric review

Wu, Y., Li, S., Li, J., Yu, Y., Li, J., & Li, Y. (2025) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2025.100144 · Cited by 31

Pixel-level detection and measurement of concrete crack using faster region-based convolutional neural network and morphological feature extraction

Li, S., & Zhao, X. (2020) · Measurement Science and Technology · doi:10.1088/1361-6501/abb274 · Cited by 28

Industrial Weak Scratches Inspection Based on Multifeature Fusion Network

Tao, X., Zhang, D., Hou, W. S., Ma, W., & Xu, D. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3025642 · Cited by 29

Arc Length method for extracting crack pattern characteristics

Asjodi, A. H., Daeizadeh, M. J., Hamidia, M., & Dolatshahi, K. M. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2653 · Cited by 28

Lightweight pixel-wise segmentation for efficient concrete crack detection using hierarchical convolutional neural network

Kim, J., Shim, S., Cha, Y., & Cho, G.-C. (2021) · Smart Materials and Structures · doi:10.1088/1361-665x/abea1e · Cited by 30

Modular Deep Learning Segmentation Algorithm for Concrete Microscopic Images

Hilloulin, B., Bekrine, I., Schmitt, E., & Loukili, A. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.128736 · Cited by 28

Automated Detection and Characterization of Cracks on Concrete Using Laser Scanning

Ge, Y., Liu, J. H.-I., Zhang, X., Tang, H., & Xia, X. (2023) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-1936 · Cited by 28

HBIM for Conservation of Built Heritage

Alshawabkeh, Y., Baik, A., & Miky, Y. H. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13070231 · Cited by 27

Applying deep convolutional neural network with 3D reality mesh model for water tank crack detection and evaluation

Wu, Z. Y., Kalfarisi, R., Kouyoumdjian, F., & Taelman, C. (2020) · Urban Water Journal · doi:10.1080/1573062x.2020.1758166 · Cited by 27

Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

Billah, U. H., La, H. M., & Tavakkoli, A. (2020) · Sensors · doi:10.3390/s20164403 · Cited by 28

Explainable AI Framework for Imaging-Based Predictive Maintenance for Automotive Applications and Beyond

Krishnamurthy, V., Nezafati, K., Stayton, E., & Singh, V. (2020) · Data-Enabled Discovery and Applications · doi:10.1007/s41688-020-00042-2 · Cited by 27

Distress Recognition in Unpaved Roads Using Unmanned Aerial Systems and Deep Learning Segmentation

Khilji, T. N., Loures, L. L. A., & Azar, E. R. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000952 · Cited by 27

Working stage identification of excavators based on control signals of operating handles

Shi, Y., Xia, Y., Luo, L., Xiong, Z., Wang, C., & Lin, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103873 · Cited by 29

Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

Li, G., Liu, T., Fang, Z., Shen, Q., & Ali, J. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.2991 · Cited by 30

Deep Learning Method to Detect the Road Cracks and Potholes for燬mart燙ities

Chu, H.-H., Saeed, M. R., Rashid, J., Mehmood, M. T., Ahmad, I., Iqbal, R. S., & Ali, G. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035287 · Cited by 32

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

Liu, C., Shirowzhan, S., Sepasgozar, S. M. E., & Kaboli, A. (2019) · Buildings · doi:10.3390/buildings9020040 · Cited by 27

Implementation of explanatory texts output for bridge damage in a bridge inspection web system

Chun, P., Chu, H.-H., Shitara, K., Yamane, T., & Maemura, Y. (2024) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2024.103706 · Cited by 26

CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function

Fan, Y., Hu, Z., Li, Q., Sun, Y., Chen, J., & Zhou, Q. (2024) · Sensors · doi:10.3390/s24227134 · Cited by 26

AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns

Azhari, S., Jamshidian, S., & Hamidia, M. (2025) · Automation in Construction · doi:10.1016/j.autcon.2024.105959 · Cited by 26

Semantic segmentation model for crack images from concrete bridges for mobile devices

Droguett, E. L., Tapia, J. E., Yáñez, C., & Boroschek, R. L. (2020) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x20965111 · Cited by 26

Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

Chaiyasarn, K., Buatik, A., & Likitlersuang, S. (2020) · Advances in Structural Engineering · doi:10.1177/1369433220975574 · Cited by 26

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Mohammed, M. A., Han, Z. S., & Li, Y. (2021) · Advances in Materials Science and Engineering · doi:10.1155/2021/9923704 · Cited by 28

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Arbaoui, A., Ouahabi, A., Jacques, S., & Hamiane, M. (2021) · Frattura ed Integrità Strutturale · doi:10.3221/igf-esis.58.03 · Cited by 26

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Deep semantic segmentation for visual scene understanding of soil types

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Crack Detection in Buildings Using Convolutional Neural NetworkPreprint

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Research on automatic detection and analysis of concrete cracks based on deep convolutional neural network architecture

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