Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

[1]  Hojjat Adeli,et al.  A novel machine learning‐based algorithm to detect damage in high‐rise building structures , 2017 .

[2]  Gabriela Csurka,et al.  What is a good evaluation measure for semantic segmentation? , 2013, BMVC.

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

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

[6]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[7]  Hadi Meidani,et al.  Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[8]  Mani Golparvar-Fard,et al.  Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment , 2014, J. Comput. Civ. Eng..

[9]  Boguslaw Cyganek,et al.  Image recognition with deep neural networks in presence of noise - Dealing with and taking advantage of distortions , 2017, Integr. Comput. Aided Eng..

[10]  Gaurav S. Sukhatme,et al.  A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures , 2009 .

[11]  Bernt Schiele,et al.  How good are detection proposals, really? , 2014, BMVC.

[12]  Ioannis Brilakis,et al.  Concrete Column Recognition in Images and Videos , 2010, J. Comput. Civ. Eng..

[13]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[14]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[15]  Sudan Xu,et al.  Segment-Based Classification of Damaged Building Roofs in Aerial Laser Scanning Data , 2013, IEEE Geoscience and Remote Sensing Letters.

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[19]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[20]  Luca Maria Gambardella,et al.  Max-pooling convolutional neural networks for vision-based hand gesture recognition , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[21]  Hongzhe Dai,et al.  A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment , 2017, Comput. Aided Civ. Infrastructure Eng..

[22]  Wei Zhang,et al.  Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization , 2018, Comput. Aided Civ. Infrastructure Eng..

[23]  Maria Q. Feng,et al.  Experimental validation of cost-effective vision-based structural health monitoring , 2017 .

[24]  Paul W. Fieguth,et al.  A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.

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

[26]  Petros Sideris.  Seismic analysis and design of precast concrete segmental bridges , 2012 .

[27]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[28]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  A. Vetrivel,et al.  Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[30]  Ming-Yu Liu,et al.  Deep Active Learning for Civil Infrastructure Defect Detection and Classification , 2017 .

[31]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[32]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Reinhold Huber-Mörk,et al.  Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images , 2014, ISVC.

[34]  Markus König,et al.  Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures , 2014 .

[35]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

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

[37]  Yicheng Li,et al.  A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects , 2018, Comput. Aided Civ. Infrastructure Eng..

[38]  Hongchi Shi,et al.  Supervised Computer-Vision-Based Sensing of Concrete Bridges for Crack-Detection and Assessment , 2014 .

[39]  Bernt Schiele,et al.  What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  ChaYoung-Jin,et al.  Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks , 2017 .

[41]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[42]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[43]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[44]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[45]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[46]  Norman Kerle,et al.  UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning , 2014 .

[47]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[49]  Rob Fergus,et al.  Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[50]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[51]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Reginald DesRoches,et al.  Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments , 2012, Adv. Eng. Informatics.

[53]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[54]  Young-Jin Cha,et al.  Deep faster R-CNN-based automated detection and localization of multiple types of damage , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[55]  Leonardo Franco,et al.  Layer multiplexing FPGA implementation for deep back-propagation learning , 2017, Integr. Comput. Aided Eng..

[56]  Ioannis Brilakis,et al.  Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation , 2011 .

[57]  Yan Xiao,et al.  Behavior of reinforced concrete columns under variable axial loads : Analysis , 2005 .

[58]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Neelima Chavali,et al.  Object-Proposal Evaluation Protocol is ‘Gameable’ , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[60]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[61]  Oral Büyüköztürk,et al.  Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..

[62]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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