Autonomous concrete crack detection using deep fully convolutional neural network

Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated. •Crack classifiers built on pre-trained networks achieve at least 97.8% in accuracy.•Semantic segmentation method produces about 90% in average precision.•Semantic segmentation method can capture crack size reasonably. … celý popis

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Publikováno v: Automation in construction Ročník 99; s. 52 - 58
Hlavní autoři: Dung, Cao Vu, Anh, Le Duc
Typ dokumentu: Journal Article
Jazyk: English
Vydáno: Amsterdam Elsevier B.V 01. 03. 2019
Elsevier BV
Témata: Artificial neural networks

Concrete

Convolutional neural network

Crack detection

Cracks

Cyclic loads

Deep learning

Encoders-Decoders

Flaw detection

Image classification

Image segmentation

Inspection

Neural networks

Pixels

Semantic segmentation

Semantics

Deep learning

Concrete

Crack detection

Semantic segmentation

Convolutional neural network
ISSN: 0926-5805
1872-7891
DOI: 10.1016/j.autcon.2018.11.028

Abstract	Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated. •Crack classifiers built on pre-trained networks achieve at least 97.8% in accuracy.•Semantic segmentation method produces about 90% in average precision.•Semantic segmentation method can capture crack size reasonably.
AbstractList	Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated. Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated. •Crack classifiers built on pre-trained networks achieve at least 97.8% in accuracy.•Semantic segmentation method produces about 90% in average precision.•Semantic segmentation method can capture crack size reasonably.
Author	Anh, Le Duc Dung, Cao Vu
Author_xml	– sequence: 1 givenname: Cao Vu surname: Dung fullname: Dung, Cao Vu email: caovu@tcu.ac.jp organization: Advanced Research Laboratories, Tokyo City University, 8-15-1 Todoroki, Setagaya, Tokyo 158-0082, Japan – sequence: 2 givenname: Le Duc surname: Anh fullname: Anh, Le Duc organization: NTT Hi-Tech Institute, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh, Ward 13, District 4, Ho Chi Minh City, Vietnam
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Cites_doi	10.1016/j.conbuildmat.2017.09.110 10.1109/TSMC.1979.4310076 10.1016/j.aei.2015.01.008 10.1007/s11263-009-0275-4 10.1061/(ASCE)CF.1943-5509.0000996 10.1109/TPAMI.2017.2699184 10.1016/j.autcon.2013.06.011 10.1109/TASE.2014.2354314 10.1109/TPAMI.2016.2577031 10.1109/TPAMI.2010.161 10.1007/s11263-015-0816-y 10.1038/nature14539 10.1061/(ASCE)CP.1943-5487.0000766 10.1109/TPAMI.2013.185 10.1111/mice.12263 10.1061/(ASCE)CP.1943-5487.0000736 10.1162/neco_a_00990 10.1177/1475921715624502
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References	Russakovsky, Deng, Su, Krause, Satheesh, Ma, Berg (bb0230) 2015; 115 Davoudi, Miller, Kutz (bb0050) 2017 ASCE, American Society of Civil Engineers (ASCE) (bb0005) 2017 Noh, Koo, Kang, Park, Lee (bb0020) 2017 Rawat, Wang (bb0070) 2017; 29 Zhang, Tan, Liu, Wu, Wang, Jie (bb0240) 2017, October Ali, Gopal, Cha (bb0035) 2018, March; Vol. 10598 Krizhevsky, Sutskever, Hinton (bb0120) 2012 Teichmann, Weber, Zoellner, Cipolla, Urtasun (bb0215) 2018, June He, Zhang, Ren, Sun (bb0135) 2016 Prasanna, Dana, Gucunski, Basily, La, Lim, Parvardeh (bb0040) 2016; 13 Cha, Choi, Büyüköztürk (bb0100) 2017; 32 Li, Zhao, Wang (bb0170) 2014 Dinh, Ha, La (bb0025) 2016 Giusti, Ciresan, Masci, Gambardella, Schmidhuber (bb0165) 2013 Zheng, Jayasumana, Romera-Paredes, Vineet, Su, Du, Torr (bb0210) 2015 Fulkerson, Vedaldi, Soatto (bb0175) 2009, September Zhang, Yang, Zhang, Zhu (bb0090) 2016, September Russell, Kohli, Torr (bb0180) 2009 Long, Shelhamer, Darrell (bb0195) 2015 Chen, Papandreou, Kokkinos, Murphy, Yuille (bb0205) 2018; 40 Pauly, Hogg, Fuentes, Peel (bb0095) 2017, July Simonyan, Zisserman (bb0125) 2014 Koch, Georgieva, Kasireddy, Akinci, Fieguth (bb0015) 2015; 29 Fan, Wu, Lu, Li (bb0080) 2018 Sato, Bao, Koya (bb0030) 2018; 8 Maeda, Sekimoto, Seto, Kashiyama, Omata (bb0155) 2018 Lecun, Bengio, Hinton (bb0225) 2015; 521 Everingham, Van Gool, Williams, Winn, Zisserman (bb0250) 2010; 88 Arbelaez, Maire, Fowlkes, Malik (bb0185) 2011; 33 Deng, Dong, Socher, Li, Li, Fei-Fei (bb0140) 2009, June Davoudi, Miller, Kutz (bb0055) 2018; 32 Cha, Choi, Suh, Mahmoudkhani, Büyüköztürk (bb0110) 2017 Chollet (bb0145) 2018 Cha, Choi (bb0105) 2017; Vol. 2 Chen, Jahanshahi (bb0075) 2017 Geiger, Lauer, Wojek, Stiller, Urtasun (bb0245) 2014; 36 Zhang, Cheng, Zhang (bb0160) 2018; 32 Badrinarayanan, Kendall, SegNet, R. C (bb0190) 2015 Camilo, Wang, Collins, Bradbury, Malof (bb0220) 2018 Ronneberger, Fischer, Brox (bb0200) 2015, October Gopalakrishnan, Khaitan, Choudhary, Agrawal (bb0150) 2017; 157 Wang, Zhang, Li, Fei, Chen, Li (bb0085) 2017 Szegedy, Vanhoucke, Ioffe, Shlens, Wojna (bb0130) 2016 Ebrahimkhanlou, Farhidzadeh, Salamone (bb0060) 2015, March; Vol. 9435 Otsu (bb0255) 1979; 9 Özgenel (bb0235) 2018; v1 Road Bureau, Ministry of Land, Infrastructure, Transportation, and Tourism (bb9650) 2015 Adhikari, Moselhi, Bagchi (bb0045) 2014; 39 Ren, He, Girshick, Sun (bb0115) 2017; 39 Cook, Barr (bb0010) 2017; 31 Ebrahimkhanlou, Farhidzadeh, Salamone (bb0065) 2016; 15 Adhikari (10.1016/j.autcon.2018.11.028_bb0045) 2014; 39 Road Bureau, Ministry of Land, Infrastructure, Transportation, and Tourism (10.1016/j.autcon.2018.11.028_bb9650) Cha (10.1016/j.autcon.2018.11.028_bb0105) 2017; Vol. 2 He (10.1016/j.autcon.2018.11.028_bb0135) 2016 Noh (10.1016/j.autcon.2018.11.028_bb0020) 2017 Prasanna (10.1016/j.autcon.2018.11.028_bb0040) 2016; 13 Chollet (10.1016/j.autcon.2018.11.028_bb0145) Fulkerson (10.1016/j.autcon.2018.11.028_bb0175) 2009 Cha (10.1016/j.autcon.2018.11.028_bb0100) 2017; 32 Russakovsky (10.1016/j.autcon.2018.11.028_bb0230) 2015; 115 ASCE, American Society of Civil Engineers (ASCE) (10.1016/j.autcon.2018.11.028_bb0005) Badrinarayanan (10.1016/j.autcon.2018.11.028_bb0190) 2015 Li (10.1016/j.autcon.2018.11.028_bb0170) 2014 Everingham (10.1016/j.autcon.2018.11.028_bb0250) 2010; 88 Özgenel (10.1016/j.autcon.2018.11.028_bb0235) 2018; v1 Russell (10.1016/j.autcon.2018.11.028_bb0180) 2009 Zheng (10.1016/j.autcon.2018.11.028_bb0210) 2015 Deng (10.1016/j.autcon.2018.11.028_bb0140) 2009 Pauly (10.1016/j.autcon.2018.11.028_bb0095) 2017 Ren (10.1016/j.autcon.2018.11.028_bb0115) 2017; 39 Ebrahimkhanlou (10.1016/j.autcon.2018.11.028_bb0065) 2016; 15 Zhang (10.1016/j.autcon.2018.11.028_bb0240) 2017 Szegedy (10.1016/j.autcon.2018.11.028_bb0130) 2016 Sato (10.1016/j.autcon.2018.11.028_bb0030) 2018; 8 Camilo (10.1016/j.autcon.2018.11.028_bb0220) 2018 Davoudi (10.1016/j.autcon.2018.11.028_bb0055) 2018; 32 Koch (10.1016/j.autcon.2018.11.028_bb0015) 2015; 29 Zhang (10.1016/j.autcon.2018.11.028_bb0160) 2018; 32 Maeda (10.1016/j.autcon.2018.11.028_bb0155) 2018 Geiger (10.1016/j.autcon.2018.11.028_bb0245) 2014; 36 Ali (10.1016/j.autcon.2018.11.028_bb0035) 2018; Vol. 10598 Rawat (10.1016/j.autcon.2018.11.028_bb0070) 2017; 29 Giusti (10.1016/j.autcon.2018.11.028_bb0165) 2013 Otsu (10.1016/j.autcon.2018.11.028_bb0255) 1979; 9 Ebrahimkhanlou (10.1016/j.autcon.2018.11.028_bb0060) 2015; Vol. 9435 Long (10.1016/j.autcon.2018.11.028_bb0195) 2015 Krizhevsky (10.1016/j.autcon.2018.11.028_bb0120) 2012 Arbelaez (10.1016/j.autcon.2018.11.028_bb0185) 2011; 33 Cha (10.1016/j.autcon.2018.11.028_bb0110) 2017 Ronneberger (10.1016/j.autcon.2018.11.028_bb0200) 2015 Wang (10.1016/j.autcon.2018.11.028_bb0085) 2017 Fan (10.1016/j.autcon.2018.11.028_bb0080) 2018 Dinh (10.1016/j.autcon.2018.11.028_bb0025) 2016 Chen (10.1016/j.autcon.2018.11.028_bb0205) 2018; 40 Gopalakrishnan (10.1016/j.autcon.2018.11.028_bb0150) 2017; 157 Lecun (10.1016/j.autcon.2018.11.028_bb0225) 2015; 521 Chen (10.1016/j.autcon.2018.11.028_bb0075) 2017 Teichmann (10.1016/j.autcon.2018.11.028_bb0215) 2018 Zhang (10.1016/j.autcon.2018.11.028_bb0090) 2016 Simonyan (10.1016/j.autcon.2018.11.028_bb0125) 2014 Davoudi (10.1016/j.autcon.2018.11.028_bb0050) 2017 Cook (10.1016/j.autcon.2018.11.028_bb0010) 2017; 31
References_xml	– start-page: 3708 year: 2016, September end-page: 3712 ident: bb0090 article-title: Road crack detection using deep convolutional neural network publication-title: Image Processing (ICIP), 2016 IEEE International Conference on – volume: 521 start-page: 436 year: 2015 ident: bb0225 article-title: Deep learning publication-title: Nature – volume: Vol. 10598 start-page: 105980L year: 2018, March ident: bb0035 article-title: Vision-based concrete crack detection technique using cascade features publication-title: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018 – volume: 31 start-page: 04017011 year: 2017 ident: bb0010 article-title: Observations and trends among collapsed bridges in New York state publication-title: J. Perform. Constr. Facil. – start-page: 248 year: 2009, June end-page: 255 ident: bb0140 article-title: ImageNet: A large-scale hierarchical image database publication-title: In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on – start-page: 234 year: 2015, October end-page: 241 ident: bb0200 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: International Conference on Medical Image Computing and Computer-assisted Intervention – volume: 29 start-page: 2352 year: 2017 end-page: 2449 ident: bb0070 article-title: Deep convolutional neural networks for image classification: a comprehensive review publication-title: Neural Comput. – volume: 8 year: 2018 ident: bb0030 article-title: Crack detection on concrete surfaces using V-shaped features publication-title: World Comp. Sci. Inform. Technol. J. – volume: 88 start-page: 303 year: 2010 end-page: 338 ident: bb0250 article-title: The pascal visual object classes (voc) challenge publication-title: Int. J. Comput. Vis. – year: 2015 ident: bb9650 article-title: Roads in Japan – volume: 40 start-page: 834 year: 2018 end-page: 848 ident: bb0205 article-title: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 2018 ident: bb0145 article-title: Keras. Github – volume: Vol. 2 start-page: 71 year: 2017 end-page: 73 ident: bb0105 article-title: Vision-based concrete crack detection using a convolutional neural network publication-title: Dynamics of Civil Structures – start-page: 4938 year: 2017, October end-page: 4943 ident: bb0240 article-title: Automatic crack inspection for concrete bridge bottom surfaces based on machine vision publication-title: Chinese Automation Congress (CAC), 2017 – start-page: 1529 year: 2015 end-page: 1537 ident: bb0210 article-title: Conditional random fields as recurrent neural networks publication-title: Computer Vision (ICCV), 2015 IEEE International Conference on – start-page: 739 year: 2009 end-page: 746 ident: bb0180 article-title: Associative hierarchical crfs for object class image segmentation publication-title: Computer Vision, 2009 IEEE 12th International Conference on – volume: 115 start-page: 211 year: 2015 end-page: 252 ident: bb0230 article-title: Imagenet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. – year: 2017 ident: bb0075 article-title: NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion publication-title: IEEE Trans. Ind. Electron. – year: 2014 ident: bb0125 article-title: Very Deep Convolutional Networks For Large-scale Image Recognition – start-page: 770 year: 2016 end-page: 778 ident: bb0135 article-title: Deep residual learning for image recognition publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – volume: 157 start-page: 322 year: 2017 end-page: 330 ident: bb0150 article-title: Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection publication-title: Constr. Build. Mater. – volume: 13 start-page: 591 year: 2016 end-page: 599 ident: bb0040 article-title: Automated crack detection on concrete bridges publication-title: IEEE Trans. Autom. Sci. Eng. – year: 2015 ident: bb0190 article-title: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation – volume: v1 year: 2018 ident: bb0235 article-title: “Concrete Crack Images for Classification”, Mendeley Data – year: 2017 ident: bb0110 article-title: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types publication-title: Comput. Aided Civ. Inf. Eng. – year: 2017 ident: bb0005 article-title: Infrastructure Report Card, 2017 – volume: 39 start-page: 180 year: 2014 end-page: 194 ident: bb0045 article-title: Image-based retrieval of concrete crack properties for bridge inspection publication-title: Autom. Constr. – start-page: 479 year: 2017, July end-page: 485 ident: bb0095 article-title: Deeper networks for pavement crack detection publication-title: Proceedings of the 34th ISARC. 34th International Symposium in Automation and Robotics in Construction, 28 Jun - 01 Jul 2017 – volume: Vol. 9435 start-page: 94351A year: 2015, March ident: bb0060 article-title: Multifractal analysis of two-dimensional images for damage assessment of reinforced concrete structures publication-title: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015 – start-page: 166 year: 2017 end-page: 177 ident: bb0085 article-title: Deep learning for asphalt pavement cracking recognition using convolutional neural network publication-title: Airfield and Highway Pavements 2017 – volume: 32 year: 2018 ident: bb0055 article-title: Structural load estimation using machine vision and surface crack patterns for shear-critical RC beams and slabs publication-title: J. Comput. Civ. Eng. – start-page: 1 year: 2016 end-page: 6 ident: bb0025 article-title: Computer vision-based method for concrete crack detection publication-title: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV) – year: 2017 ident: bb0050 article-title: Computer vision based inspection approach to predict damage state and load level for RC members publication-title: Structural Health Monitoring 2017, (shm) – volume: 32 start-page: 04018001 year: 2018 ident: bb0160 article-title: Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning publication-title: J. Comput. Civ. Eng. – start-page: 670 year: 2009, September end-page: 677 ident: bb0175 article-title: Class segmentation and object localization with superpixel neighborhoods publication-title: Computer Vision, 2009 IEEE 12th International Conference on – year: 2018 ident: bb0080 article-title: Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network – start-page: 2818 year: 2016 end-page: 2826 ident: bb0130 article-title: Rethinking the inception architecture for computer vision publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – year: 2018 ident: bb0220 article-title: Application of a Semantic Segmentation Convolutional Neural Network for Accurate Automatic Detection and Mapping of Solar Photovoltaic Arrays in Aerial Imagery – volume: 32 start-page: 361 year: 2017 end-page: 378 ident: bb0100 article-title: Deep learning-based crack damage detection using convolutional neural networks publication-title: Comput. Aided Civ. Inf. Eng. – start-page: 3431 year: 2015 end-page: 3440 ident: bb0195 article-title: Fully convolutional networks for semantic segmentation publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – volume: 15 start-page: 81 year: 2016 end-page: 92 ident: bb0065 article-title: Multifractal analysis of crack patterns in reinforced concrete shear walls publication-title: Struct. Health Monit. – volume: 29 start-page: 196 year: 2015 end-page: 210 ident: bb0015 article-title: A review on computer vision-based defect detection and condition assessment of concrete and asphalt civil infrastructure publication-title: Adv. Eng. Inform. – start-page: 4034 year: 2013 end-page: 4038 ident: bb0165 article-title: Fast image scanning with deep max-pooling convolutional neural networks publication-title: Image Processing (ICIP), 2013 20th IEEE International Conference on – volume: 39 start-page: 1137 year: 2017 end-page: 1149 ident: bb0115 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 9 start-page: 62 year: 1979 end-page: 66 ident: bb0255 article-title: A threshold selection method from gray-level histograms publication-title: IEEE Trans. Syst. Man Cybern. – volume: 33 start-page: 898 year: 2011 end-page: 916 ident: bb0185 article-title: Contour detection and hierarchical image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 1013 year: 2018, June end-page: 1020 ident: bb0215 article-title: Multinet: real-time joint semantic reasoning for autonomous driving publication-title: 2018 IEEE Intelligent Vehicles Symposium (IV) – volume: 36 start-page: 1012 year: 2014 end-page: 1025 ident: bb0245 article-title: 3d traffic scene understanding from movable platforms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 2014 ident: bb0170 article-title: Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks For Pixelwise Classification – start-page: 877 year: 2017 end-page: 880 ident: bb0020 article-title: Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering publication-title: Applied System Innovation (ICASI), 2017 International Conference on – start-page: 1097 year: 2012 end-page: 1105 ident: bb0120 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Proces. Syst. – year: 2018 ident: bb0155 article-title: Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone – start-page: 234 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0200 article-title: U-net: Convolutional networks for biomedical image segmentation – volume: 157 start-page: 322 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0150 article-title: Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2017.09.110 – year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0050 article-title: Computer vision based inspection approach to predict damage state and load level for RC members – volume: 9 start-page: 62 issue: 1 year: 1979 ident: 10.1016/j.autcon.2018.11.028_bb0255 article-title: A threshold selection method from gray-level histograms publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1979.4310076 – year: 2014 ident: 10.1016/j.autcon.2018.11.028_bb0170 – volume: 29 start-page: 196 issue: 2 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0015 article-title: A review on computer vision-based defect detection and condition assessment of concrete and asphalt civil infrastructure publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2015.01.008 – volume: 88 start-page: 303 issue: 2 year: 2010 ident: 10.1016/j.autcon.2018.11.028_bb0250 article-title: The pascal visual object classes (voc) challenge publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-009-0275-4 – volume: Vol. 10598 start-page: 105980L year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0035 article-title: Vision-based concrete crack detection technique using cascade features – volume: v1 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0235 – start-page: 877 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0020 article-title: Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering – volume: 31 start-page: 04017011 issue: 4 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0010 article-title: Observations and trends among collapsed bridges in New York state publication-title: J. Perform. Constr. Facil. doi: 10.1061/(ASCE)CF.1943-5509.0000996 – volume: 40 start-page: 834 issue: 4 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0205 article-title: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2699184 – start-page: 1529 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0210 article-title: Conditional random fields as recurrent neural networks – volume: 39 start-page: 180 year: 2014 ident: 10.1016/j.autcon.2018.11.028_bb0045 article-title: Image-based retrieval of concrete crack properties for bridge inspection publication-title: Autom. Constr. doi: 10.1016/j.autcon.2013.06.011 – start-page: 1013 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0215 article-title: Multinet: real-time joint semantic reasoning for autonomous driving – start-page: 248 year: 2009 ident: 10.1016/j.autcon.2018.11.028_bb0140 article-title: ImageNet: A large-scale hierarchical image database – start-page: 2818 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0130 article-title: Rethinking the inception architecture for computer vision publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – volume: 13 start-page: 591 issue: 2 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0040 article-title: Automated crack detection on concrete bridges publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2014.2354314 – start-page: 3708 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0090 article-title: Road crack detection using deep convolutional neural network – volume: 39 start-page: 1137 issue: 6 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0115 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2577031 – year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0155 – start-page: 670 year: 2009 ident: 10.1016/j.autcon.2018.11.028_bb0175 article-title: Class segmentation and object localization with superpixel neighborhoods – start-page: 1 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0025 article-title: Computer vision-based method for concrete crack detection – volume: 8 issue: 1 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0030 article-title: Crack detection on concrete surfaces using V-shaped features publication-title: World Comp. Sci. Inform. Technol. J. – volume: 33 start-page: 898 issue: 5 year: 2011 ident: 10.1016/j.autcon.2018.11.028_bb0185 article-title: Contour detection and hierarchical image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.161 – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0230 article-title: Imagenet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-015-0816-y – year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0080 – year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0110 article-title: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types publication-title: Comput. Aided Civ. Inf. Eng. – start-page: 4034 year: 2013 ident: 10.1016/j.autcon.2018.11.028_bb0165 article-title: Fast image scanning with deep max-pooling convolutional neural networks – volume: Vol. 2 start-page: 71 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0105 article-title: Vision-based concrete crack detection using a convolutional neural network – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0225 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 32 issue: 4 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0055 article-title: Structural load estimation using machine vision and surface crack patterns for shear-critical RC beams and slabs publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000766 – volume: 36 start-page: 1012 issue: 5 year: 2014 ident: 10.1016/j.autcon.2018.11.028_bb0245 article-title: 3d traffic scene understanding from movable platforms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.185 – start-page: 479 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0095 article-title: Deeper networks for pavement crack detection – start-page: 770 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0135 article-title: Deep residual learning for image recognition publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – ident: 10.1016/j.autcon.2018.11.028_bb0005 – start-page: 1097 year: 2012 ident: 10.1016/j.autcon.2018.11.028_bb0120 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Proces. Syst. – volume: 32 start-page: 361 issue: 5 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0100 article-title: Deep learning-based crack damage detection using convolutional neural networks publication-title: Comput. Aided Civ. Inf. Eng. doi: 10.1111/mice.12263 – start-page: 739 year: 2009 ident: 10.1016/j.autcon.2018.11.028_bb0180 article-title: Associative hierarchical crfs for object class image segmentation – ident: 10.1016/j.autcon.2018.11.028_bb9650 – start-page: 4938 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0240 article-title: Automatic crack inspection for concrete bridge bottom surfaces based on machine vision – year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0075 article-title: NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion publication-title: IEEE Trans. Ind. Electron. – start-page: 166 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0085 article-title: Deep learning for asphalt pavement cracking recognition using convolutional neural network – volume: 32 start-page: 04018001 issue: 2 year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0160 article-title: Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000736 – year: 2018 ident: 10.1016/j.autcon.2018.11.028_bb0220 – volume: 29 start-page: 2352 issue: 9 year: 2017 ident: 10.1016/j.autcon.2018.11.028_bb0070 article-title: Deep convolutional neural networks for image classification: a comprehensive review publication-title: Neural Comput. doi: 10.1162/neco_a_00990 – ident: 10.1016/j.autcon.2018.11.028_bb0145 – year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0190 – start-page: 3431 year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0195 article-title: Fully convolutional networks for semantic segmentation publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – year: 2014 ident: 10.1016/j.autcon.2018.11.028_bb0125 – volume: Vol. 9435 start-page: 94351A year: 2015 ident: 10.1016/j.autcon.2018.11.028_bb0060 article-title: Multifractal analysis of two-dimensional images for damage assessment of reinforced concrete structures – volume: 15 start-page: 81 issue: 1 year: 2016 ident: 10.1016/j.autcon.2018.11.028_bb0065 article-title: Multifractal analysis of crack patterns in reinforced concrete shear walls publication-title: Struct. Health Monit. doi: 10.1177/1475921715624502
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Snippet	Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been...
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StartPage	52
SubjectTerms	Artificial neural networks Concrete Convolutional neural network Crack detection Cracks Cyclic loads Deep learning Encoders-Decoders Flaw detection Image classification Image segmentation Inspection Neural networks Pixels Semantic segmentation Semantics
Title	Autonomous concrete crack detection using deep fully convolutional neural network
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Autonomous concrete crack detection using deep fully convolutional neural network

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