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Nhat-Duc Hoang (2019)
Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regressionAutomation in Construction
Christian Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, Scott Reed, Dragomir Anguelov, D. Erhan, Vincent Vanhoucke, Andrew Rabinovich (2014)
Going deeper with convolutions2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ankang Ji, X. Xue, Yuna Wang, Xiaowei Luo, Weirui Xue (2020)
An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavementAutomation in Construction
Yann LeCun, L. Bottou, Yoshua Bengio, P. Haffner (1998)
Gradient-based learning applied to document recognitionProc. IEEE, 86
M. Rafiei, H. Adeli (2017)
A New Neural Dynamic Classification AlgorithmIEEE Transactions on Neural Networks and Learning Systems, 28
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image SegmentationArXiv, abs/1505.04597
Xavier Glorot, Yoshua Bengio (2010)
Understanding the difficulty of training deep feedforward neural networks
TensorFlow : Large - scale machine learning on heterogeneous systems
Nicolas Pinto, David Cox, J. DiCarlo (2008)
Why is Real-World Visual Object Recognition Hard?PLoS Computational Biology, 4
Ha-won Song, Seung-Jun Kwon, K. Byun, Chankyu Park (2006)
Predicting carbonation in early-aged cracked concreteCement and Concrete Research, 36
A. Talab, Zhangcan Huang, Fang Xi, HaiMing Liu (2016)
Detection crack in image using Otsu method and multiple filtering in image processing techniquesOptik, 127
T. Nishikawa, J. Yoshida, T. Sugiyama, Y. Fujino (2012)
Concrete Crack Detection by Multiple Sequential Image FilteringComputer‐Aided Civil and Infrastructure Engineering, 27
M. Rafiei, W. Khushefati, R. Demirboga, H. Adeli (2017)
Supervised Deep Restricted Boltzmann Machine for Estimation of ConcreteMaterials, 114
Cao Dung, L. Anh (2019)
Autonomous concrete crack detection using deep fully convolutional neural networkAutomation in Construction
Yujing Jiang, Xuepeng Zhang, Tetsuya Taniguchi (2019)
Quantitative condition inspection and assessment of tunnel liningAutomation in Construction
François Chollet (2016)
Xception: Deep Learning with Depthwise Separable Convolutions2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Xincong Yang, Heng Li, Yantao Yu, Xiaochun Luo, Ting Huang, Xu Yang (2018)
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional NetworkComputer‐Aided Civil and Infrastructure Engineering, 33
Hui Zhang, Jinwen Tan, Li Liu, Q. Wu, Yaonan Wang, Liu Jie (2017)
Automatic crack inspection for concrete bridge bottom surfaces based on machine vision2017 Chinese Automation Congress (CAC)
Tran Dinh, Q. Ha, Hung La (2016)
Computer vision-based method for concrete crack detection2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)
D. Ciresan, U. Meier, J. Schmidhuber (2012)
Multi-column deep neural networks for image classification2012 IEEE Conference on Computer Vision and Pattern Recognition
Danilo Pereira, M. Piteri, A. Souza, J. Papa, H. Adeli (2019)
FEMa: a finite element machine for fast learningNeural Computing and Applications, 32
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)
Deep LearningNature, 521
Rui Fan, M. Bocus, Yilong Zhu, Jianhao Jiao, Li Wang, Fulong Ma, Shanshan Cheng, Meilin Liu (2019)
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding2019 IEEE Intelligent Vehicles Symposium (IV)
Ahmad Hassanpour, Majid Moradikia, H. Adeli, Seyed Khayami, Pirooz Shamsinejadbabaki (2019)
A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signalsExpert Systems, 36
(2009)
This PDF file includes: Materials and Methods
FuTao Ni, Jian Zhang, Zhiqiang Chen (2018)
Pixel‐level crack delineation in images with convolutional feature fusionStructural Control and Health Monitoring, 26
M. Ahmadlou, H. Adeli (2010)
Enhanced probabilistic neural network with local decision circles: A robust classifierIntegr. Comput. Aided Eng., 17
Ç. Özgenel (2019)
Concrete Crack Segmentation Dataset, 1
Hong-wei Huang, Qing-tong Li, Dongming Zhang (2018)
Deep learning based image recognition for crack and leakage defects of metro shield tunnelTunnelling and Underground Space Technology
(2022)
Sparse-sensing and superpixel-based segmentation model for concrete cracks
Elisabeth Menendez, J. Victores, Roberto Montero, S. Martinez, C. Balaguer (2018)
Tunnel structural inspection and assessment using an autonomous robotic systemAutomation in Construction, 87
Zhenqing Liu, Yiwen Cao, Yize Wang, Wei Wang (2019)
Computer vision-based concrete crack detection using U-net fully convolutional networksAutomation in Construction
P. Prasanna, Kristin Dana, N. Gucunski, B. Basily, H. La, R. Lim, H. Parvardeh (2016)
Automated Crack Detection on Concrete BridgesIEEE Transactions on Automation Science and Engineering, 13
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Deep Residual Learning for Image Recognition2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification2015 IEEE International Conference on Computer Vision (ICCV)
Jianhong Wang, Zihai Shi, M. Nakano (2013)
Strength degradation analysis of an aging RC girder bridge using FE crack analysis and simple capacity-evaluation equationsEngineering Fracture Mechanics, 108
Evan Shelhamer, Jonathan Long, Trevor Darrell (2014)
Fully convolutional networks for semantic segmentation2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hong-wei Huang, Shuai Zhao, Dongming Zhang, Jiayao Chen (2020)
Deep learning-based instance segmentation of cracks from shield tunnel lining imagesStructure and Infrastructure Engineering, 18
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2016)
Identity Mappings in Deep Residual Networks
Dongho Kang, Y. Cha (2021)
Efficient attention-based deep encoder and decoder for automatic crack segmentationStructural Health Monitoring, 21
David Stutz, Alexander Hermans, B. Leibe (2016)
Superpixels: An evaluation of the state-of-the-artComput. Vis. Image Underst., 166
M. Rafiei, W. Khushefati, R. Demirboga, H. Adeli (2016)
Neural Network, Machine Learning, and Evolutionary Approaches for Concrete Material CharacterizationMaterials, 113
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár (2017)
Focal Loss for Dense Object Detection2017 IEEE International Conference on Computer Vision (ICCV)
Y. Cha, Wooram Choi, O. Büyüköztürk (2017)
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural NetworksComputer‐Aided Civil and Infrastructure Engineering, 32
M. Rafiei, H. Adeli (2016)
A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate UnitsJournal of Construction Engineering and Management-asce, 142
Huapeng Chen (2017)
Monitoring-Based Reliability Analysis of Aging Concrete Structures by Bayesian UpdatingJournal of Aerospace Engineering, 30
Xing Hao, Guigang Zhang, Shang Ma (2016)
Deep LearningInt. J. Semantic Comput., 10
B. Lee, Y. Kim, S. Yi, Jin-keun Kim (2013)
Automated image processing technique for detecting and analysing concrete surface cracksStructure and Infrastructure Engineering, 9
Yohwan Noh, Donghyun Koo, Yong-Min Kang, DongGyu Park, Dohoon Lee (2017)
Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering2017 International Conference on Applied System Innovation (ICASI)
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander Alemi (2016)
Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningArXiv, abs/1602.07261
Y. Cha, Wooram Choi, Gahyun Suh, S. Mahmoudkhani, O. Büyüköztürk (2018)
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage TypesComputer‐Aided Civil and Infrastructure Engineering, 33
Ruochen Liu, Dawei Jiang, Langlang Zhang, Zetong Zhang (2020)
Deep Depthwise Separable Convolutional Network for Change Detection in Optical Aerial ImagesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13
V. Gribniak, A. Caldentey, G. Kaklauskas, Arvydas Rimkus, A. Sokolov (2016)
Effect of arrangement of tensile reinforcement on flexural stiffness and crackingEngineering Structures, 124
Liang-Chieh Chen, Yukun Zhu, G. Papandreou, Florian Schroff, Hartwig Adam (2018)
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
E. Cubuk, Barret Zoph, Jonathon Shlens, Quoc Le (2019)
Randaugment: Practical automated data augmentation with a reduced search space2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Diederik Kingma, Jimmy Ba (2014)
Adam: A Method for Stochastic OptimizationCoRR, abs/1412.6980
Raza Ali, Joon Chuah, M. Talip, N. Mokhtar, M. Shoaib (2022)
Structural crack detection using deep convolutional neural networksAutomation in Construction
Nian Zhang, X. Zhu, Y. Ren (2019)
Analysis and Study on Crack Characteristics of Highway Tunnel LiningCivil Engineering Journal
V. Gribniak, Arvydas Rimkus, A. Caldentey, A. Sokolov (2020)
Cracking of concrete prisms reinforced with multiple bars in tension–the cover effectEngineering Structures, 220
Jacob König, M. Jenkins, P. Barrie, M. Mannion, G. Morison (2019)
A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating2019 IEEE International Conference on Image Processing (ICIP)
Hyunjun Kim, Eunjong Ahn, Myoungsu Shin, S. Sim (2019)
Crack and Noncrack Classification from Concrete Surface Images Using Machine LearningStructural Health Monitoring, 18
Hong-wei Huang, Y. Sun, Ya-dong Xue, Fei Wang (2017)
Inspection equipment study for subway tunnel defects by grey-scale image processingAdv. Eng. Informatics, 32
T. Sahoo (2013)
Inspection of Equipment
Kamal Sarma, H. Adeli (1999)
Cost Optimization of Concrete StructuresJournal of Structural Engineering-asce, 124
Ross Girshick, Jeff Donahue, Trevor Darrell, J. Malik (2013)
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation2014 IEEE Conference on Computer Vision and Pattern Recognition
I. Abdel-Qader, O. Abudayyeh, Michael Kelly (2003)
ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGESJournal of Computing in Civil Engineering, 17
M. Rafiei, H. Adeli (2017)
NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimizationSoil Dynamics and Earthquake Engineering, 100
Hae‐Bum Yun, Soroush Mokhtari, Liuliu Wu (2015)
Crack Recognition and Segmentation Using Morphological Image-Processing Techniques for Flexible PavementsTransportation Research Record, 2523
Shengyuan Li, Xuefeng Zhao, Guangyi Zhou (2019)
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional networkComputer‐Aided Civil and Infrastructure Engineering, 34
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Z. Wojna (2015)
Rethinking the Inception Architecture for Computer Vision2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
J. Schmidhuber (2014)
Deep learning in neural networks: An overviewNeural networks : the official journal of the International Neural Network Society, 61
Nhat-Duc Hoang, Quoc-Lam Nguyen, Xuan-Linh Tran (2019)
Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture AnalysisComplex., 2019
Andreas Veit, Michael Wilber, Serge Belongie (2016)
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Zongwei Zhou, M. Siddiquee, Nima Tajbakhsh, Jianming Liang (2019)
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image SegmentationIEEE Transactions on Medical Imaging, 39
Sang-soon Park, Seung-Jun Kwon, Sanghwa Jung (2012)
Analysis technique for chloride penetration in cracked concrete using equivalent diffusion and permeationConstruction and Building Materials, 29
K. Alam, N. Siddique, H. Adeli (2019)
A dynamic ensemble learning algorithm for neural networksNeural Computing and Applications, 32
J. Canny (1986)
A Computational Approach to Edge DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
Y. Fujita, Y. Hamamoto (2011)
A robust automatic crack detection method from noisy concrete surfacesMachine Vision and Applications, 22
Guilherme Martins, J. Papa, H. Adeli (2020)
Deep learning techniques for recommender systems based on collaborative filteringExpert Systems, 37
Yahui Liu, Jian Yao, Xiaohu Lu, Renping Xie, Li Li (2019)
DeepCrack: A deep hierarchical feature learning architecture for crack segmentationNeurocomputing, 338
Efficient image‐recognition algorithms to classify the pixels accurately are required for the computer‐vision‐based inspection of concrete defects. This study proposes a deep learning‐based model called sparse‐sensing and superpixel‐based segmentation (SSSeg) for accurate and efficient crack segmentation. The model employed a sparse‐sensing‐based encoder and a superpixel‐based decoder and was compared with six state‐of‐the‐art models. An input pipeline of 1231 diverse crack images was specially designed to train and evaluate the models. The results indicated that the SSSeg achieved a good balance between the recognition correctness and completeness and outperformed other models in both accuracy and efficiency. The SSSeg also exhibited good resistance to the interference of surface roughness, dirty stains, and moisture. The increased depth and receptive field of sparse‐sensing units guaranteed the representability; meanwhile, structured sparse characteristics protected the network from overfitting. The lightweight superpixel‐based decoder omitted skip connections, which greatly reduced the computation and memory footprint and enlarged the input size in the inference.
Computer-Aided Civil and Infrastructure Engineering – Wiley
Published: Nov 1, 2022
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