[1]  ASCE’s 2017 infrastructure report card: bridges [Internet]. Reston: American Society of Civil Engineers; 2017 [cited 2017 Oct 11]. Available from: https:// www.infrastructurereportcard.org/cat-item/bridges/.

[2]  R. P. Gallagher Associates, Inc. ATC-20-1 Field manual: postearthquake safety evaluation of buildings (second edition). Redwood City: Applied Technology Council; 2005. 链接1

[3]  Federal Highway Administration, Department of Transportation. National bridge inspection standards. Fed Regist [Internet] 2004 Dec [cited 2018 Jul 30];69(239):74419–39. Available from: https://www.govinfo.gov/content/ pkg/FR-2004-12-14/pdf/04-27355.pdf.

[4]  Corps of Engineers inspection team goes to new heights. Washington, DC: US Army Corps of Engineers; 2012.

[5]  Ou J, Li H. Structural health monitoring in mainland China: review and future trends. Struct Health Monit 2010;9(3):219–31. 链接1

[6]  Carden EP, Fanning P. Vibration based condition monitoring: a review. Struct Health Monit 2004;3(4):355–77. 链接1

[7]  Brownjohn JMW. Structural health monitoring of civil infrastructure. Philos Trans A Math Phys Eng Sci 1851;2007(365):589–622. 链接1

[8]  Li HN, Ren L, Jia ZG, Yi TH, Li DS. State-of-the-art in structural health monitoring of large and complex civil infrastructures. J Civ Struct Heal Monit 2016;6(1):3–16. 链接1

[9]  Roberts LG. Machine perception of three-dimensional solids [dissertation]. Cambridge: Massachusetts Institute of Technology; 1963. 链接1

[10]  Postal mechanization and early automation [Internet]. Washington, DC: United States Postal Service; 1956 [cited 2018 Jun 22]. Available from: https://about.usps.com/publications/pub100/pub100_042.htm.

[11]  Szeliski R. Computer vision: algorithms and applications. London: Springer; 2011. 链接1

[12]  Viola P, Jones MJ. Robust real-time face detection. Int J Comput Vis 2004;57 (2):137–54. 链接1

[13]  Turk MA, Pentland AP. Face recognition using eigenfaces. In: Proceedings of 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 1991 Jun 3–6; Maui, HI, USA. Piscataway: IEEE; 1991. p. 586–91. 链接1

[14]  Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2005 Jun 20–25; San Diego, CA, USA. Piscataway: IEEE; 2005. p. 886–93. 链接1

[15]  Pingali G, Opalach A, Jean Y. Ball tracking and virtual replays for innovative tennis broadcasts. In: Proceedings of the 15th International Conference on Pattern Recognition; 2000 Sep 3–7; Barcelona, Spain. Piscataway: IEEE; 2000. p. 152–6. 链接1

[16]  Bishop CM. Pattern recognition and machine learning. New York City: Springer-Verlag; 2006. 链接1

[17]  Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Gordon G, Dunson D, Dudík M, editors. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics; 2011 Apr 11–13; Fort Lauderdale, FL, USA; 2011. p. 315–23. 链接1

[18]  Rumelhart DE, Hinton GE, Williams RJ. Learning representations by backpropagating errors. Nature 1986;323(6088):533–6. 链接1

[19]  Kingma DP, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations; 2015 May 7–9; San Diego, CA, USA; 2015. p. 1–15. 链接1

[20]  LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278–324. 链接1

[21]  Wu S, Zhong S, Liu Y. Deep residual learning for image steganalysis. Multimed Tools Appl 2018;77(9):10437–53. 链接1

[22]  Karpathy A. t-SNE visualization of CNN codes [Internet]. [cited 2018 Jul 27]. Available from: https://cs.stanford.edu/people/karpathy/cnnembed/.

[23]  Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition; 2015 Jun 7–12; Boston, MA, USA. Piscataway: IEEE; 2015. p. 3431–40. 链接1

[24]  Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 2013;35(8):1915–29. 链接1

[25]  Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39(12):2481–95. 链接1

[26]  Couprie C, Farabet C, Najman L, LeCun Y. Indoor semantic segmentation using depth information. arXiv:1301.3572. 2013.

[27]  He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors. Computer vision—ECCV 2014. Cham: Springer; 2014. p. 346–61. 链接1

[28]  Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition; 2014 Jun 23–28; Columbus, OH, USA. Piscataway: IEEE; 2014. p. 580–7. 链接1

[29]  Girshick R. Fast R-CNN. In: Proceedings of 2015 IEEE International Conference on Computer Vision; 2015 Dec 7–13; Santiago, Chile. Piscataway: IEEE; 2015. p. 1440–8. 链接1

[30]  Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017;39(6):1137–49. 链接1

[31]  Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, realtime object detection. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 779–88. 链接1

[32]  He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of 2017 IEEE International Conference on Computer Vision; 2017 Oct 22–29; Venice, Italy. Piscataway: IEEE; 2017. p. 2980–8. 链接1

[33]  De Brabandere B, Neven D, Van Gool L. Semantic instance segmentation with a discriminative loss function. arXiv:1708.02551. 2017.

[34]  Zhang Z, Schwing AG, Fidler S, Urtasun R. Monocular object instance segmentation and depth ordering with CNNs. arXiv:1505.03159. 2015.

[35]  Werbos PJ. Backpropagation through time: what it does and how to do it. Proc IEEE 1990;78(10):1550–60. 链接1

[36]  Perazzi F, Khoreva A, Benenson R, Schiele B, Sorkine-Hornung A. Learning video object segmentation from static images. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition; 2017 Jul 21–26; Honolulu, HI, USA. Piscataway: IEEE; 2017. p. 2663–72. 链接1

[37]  Nilsson D, Sminchisescu C. Semantic video segmentation by gated recurrent flow propagation. arXiv:1612.08871. 2016.

[38]  Labelme.csail.mit.edu [Interent]. Cambridge: MIT Computer Science and Artificial Intelligence Laboratory; [cited 2018 Aug 1]. Available from: http:// labelme.csail.mit.edu/Release3.0/.

[39]  Hess MR, Petrovic V, Kuester F. Interactive classification of construction materials: feedback driven framework for annotation and analysis of 3D point clouds. In: Hayes J, Ouimet C, Santana Quintero M, Fai S, Smith L, editors. Proceedings of ICOMOS/ISPRS International Scientific Committee on Heritage Documentation (volume XLII-2/W5); 2017 Aug 28–Sep 1; Ottawa, ON, Canada; 2017. p. 343–7. 链接1

[40]  Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 2921–9. 链接1

[41]  Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of 2017 IEEE International Conference on Computer Vision; 2017 Oct 22–29; Venice, Italy. Piscataway: IEEE; 2017. p. 618–26. 链接1

[42]  DeGol J, Golparvar-Fard M, Hoiem D. Geometry-informed material recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 1554–62. 链接1

[43]  Hinton GE. Training products of experts by minimizing contrastive divergence. Neural Comput 2002;14(8):1771–800. 链接1

[44]  Salakhutdinov R, Larochelle H. Efficient learning of deep Boltzmann machines. In: Teh YW, Titterington M, editors. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics; 2010 May 13–15; Sardinia, Italy; 2010. p. 693–700. 链接1

[45]  Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313(5786):504–7. 链接1

[46]  Pathak D, Krähenbühl P, Donahue J, Darrell T, Efros AA. Context encoders: feature learning by inpainting. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 2536–44. 链接1

[47]  Zhang R, Isola P, Efros AA. Split-brain autoencoders: unsupervised learning by cross-channel prediction. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition; 2017 Jul 21–26; Honolulu, HI, USA. Piscataway: IEEE; 2017. p. 645–54. 链接1

[48]  Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. 2015.

[49]  Denton E, Chintala S, Szlam A, Fergus R. Deep generative image models using a Laplacian pyramid of adversarial networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems: volume 1; 2015 Dec 7–12; Montreal, QC, Canada. Cambridge: MIT Press; 2015. p. 1486–94. 链接1

[50]  Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training GANs. In: Proceedings of the 30th International Conference on Neural Information Processing Systems; 2016 Dec 5–10; Barcelona, Spain. New York City: Curran Associates Inc.; 2016. p. 2234–42. 链接1

[51]  LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. 链接1

[52]  Barron JL, Fleet DJ, Beauchemin SS. Performance of optical flow techniques. Int J Comput Vis 1994;12(1):43–77. 链接1

[53]  Richardson IEG. H.264 and MPEG-4 video compression: video coding for next-generation multimedia. Chichester: John Wiley & Sons, Ltd.; 2003. 链接1

[54]  Grundmann M, Kwatra V, Han M, Essa I. Efficient hierarchical graph-based video segmentation. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010 Jun 13–18; San Francisco, CA, USA. Piscataway: IEEE; 2010. p. 2141–8. 链接1

[55]  Wadhwa N, Rubinstein M, Durand F, Freeman WT. Phase-based video motion processing. ACM Trans Graph 2013;32(4):80:1–9. 链接1

[56]  Engel J, Sturm J, Cremers D. Camera-based navigation of a low-cost quadrocopter. In: Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2012 Oct 7–12; Vilamoura, Portugal. Piscataway: IEEE; 2012. p. 2815–21. 链接1

[57]  Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, et al. End to end learning for self-driving cars. arXiv:1604.07316v1. 2016.

[58]  Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet V. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv:1312.6082v4. 2014.

[59]  Templeton B, inventor; Google LLC, Waymo LLC, assignees. Methods and systems for transportation to destinations by a self-driving vehicle. United States Patent US 9665101B1. 2017 May 30.

[60]  Taigman Y, Yang M, Ranzato MA, Wolf L. DeepFace: closing the gap to humanlevel performance in face verification. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition; 2014 Jun 23–28; Columbus, OH, USA. Piscataway: IEEE; 2014. p. 1701–8. 链接1

[61]   Airport’s facial recognition technology catches 2 people attempting to enter US illegally [Internet]. New York City: FOX News Network, LLC.; 2018 Sep 12 [cited 2018 Sep 25]. Available from: http://www.foxnews.com/travel/2018/ 09/12/airports-facial-recognition-technology-catches-2-people-attemptingto-enter-us-illegally.html.

[62]  Wojna Z, Gorban AN, Lee DS, Murphy K, Yu Q, Li Y, et al. Attention-based extraction of structured information from street view imagery. In: Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition: volume 1; 2017 Nov 9–15; Kyoto, Japan. Piscataway: IEEE; 2018. p. 844–50. 链接1

[63]  Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88. 链接1

[64]  Zink J, Lovelace B. Unmanned aerial vehicle bridge inspection demonstration project: final report. St. Paul: Minnesota Department of Transportation; 2015 Jul. Report No.: MN/RC 2015-40.

[65]  Wells J, Lovelace B. Unmanned aircraft system bridge inspection demonstration project phase II: final report. St. Paul: Minnesota Department of Transportation; 2017 Jun. Report No.: MN/RC 2017-18.

[66]  Otero LD, Gagliardo N, Dalli D, Huang WH, Cosentino P. Proof of concept for using unmanned aerial vehicles for high mast pole and bridge inspections: final report. Tallahassee: Florida Department of Transportation; 2015 Jun. Contract No.: BDV28 TWO 977-02.

[67]  Tomiczek AP, Bridge JA, Ifju PG, Whitley TJ, Tripp CS, Ortega AE, et al. Small unmanned aerial vehicle (sUAV) inspections in GPS denied area beneath bridges. In: Soules JG, editor. Structures Congress 2018: bridges, transportation structures, and nonbuilding structures; 2018 Apr 19–21; Fort Worth, TX, USA. Reston: American Society of Civil Engineers; 2018. p. 205–16. 链接1

[68]  Brooks C, Dobson RJ, Banach DM, Dean D, Oommen T, Wolf RE, et al. Evaluating the use of unmanned aerial vehicles for transportation purposes: final report. Lansing: Michigan Department of Transportation; 2015 May. Report No.: RC-1616, MTRI-MDOTUAV-FR-2014. Contract No.: 2013-067.

[69]  Duque L, Seo J, Wacker J. Synthesis of unmanned aerial vehicle applications for infrastructures. J Perform Constr Facil 2018;32(4):04018046. 链接1

[70]  Abdel-Qader I, Abudayyeh O, Kelly ME. Analysis of edge-detection techniques for crack identification in bridges. J Comput Civ Eng 2003;17(4):255–63. 链接1

[71]  Jahanshahi MR, Kelly JS, Masri SF, Sukhatme GS. A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Struct Infrastruct Eng 2009;5(6):455–86. 链接1

[72]  Jahanshahi MR, Masri SF. A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation. Smart Mater Struct 2013;22(3):035019. 链接1

[73]  Zhu Z, German S, Brilakis I. Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation. Autom Construct 2011;20(7):874–83. 链接1

[74]  Nishikawa T, Yoshida J, Sugiyama T, Fujino Y. Concrete crack detection by multiple sequential image filtering. Comput Civ Infrastruct Eng 2012;27 (1):29–47. 链接1

[75]  Yamaguchi T, Hashimoto S. Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach Vis Appl 2010;21(5):797–809. 链接1

[76]  Zhang W, Zhang Z, Qi D, Liu Y. Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors (Basel) 2014;14 (10):19307–28. 链接1

[77]  Olsen MJ, Chen Z, Hutchinson T, Kuester F. Optical techniques for multiscale damage assessment. Geomat Nat Haz Risk 2013;4(1):49–70. 链接1

[78]  Lee BY, Kim YY, Yi ST, Kim JK. Automated image processing technique for detecting and analysing concrete surface cracks. Struct Infrastruct Eng 2013;9 (6):567–77. 链接1

[79]  Liu YF, Cho SJ, Spencer BF Jr, Fan JS. Automated assessment of cracks on concrete surfaces using adaptive digital image processing. Smart Struct Syst 2014;14(4):719–41. 链接1

[80]  Liu YF, Cho SJ, Spencer BF Jr, Fan JS. Concrete crack assessment using digital image processing and 3D scene reconstruction. J Comput Civ Eng 2016;30 (1):04014124. 链接1

[81]  Jahanshahi MR, Masri SF, Padgett CW, Sukhatme GS. An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach Vis Appl 2013;24(2):227–41. 链接1

[82]  Erkal BG, Hajjar JF. Laser-based surface damage detection and quantification using predicted surface properties. Autom Construct 2017;83:285–302. 链接1

[83]  Kim H, Ahn E, Cho S, Shin M, Sim SH. Comparative analysis of image binarization methods for crack identification in concrete structures. Cement Concr Res 2017;99:53–61. 链接1

[84]  Prasanna P, Dana KJ, Gucunski N, Basily BB, La HM, Lim RS, et al. Automated crack detection on concrete bridges. IEEE Trans Autom Sci Eng 2016;13 (2):591–9. 链接1

[85]  Adhikari RS, Moselhi O, Bagchi A. Image-based retrieval of concrete crack properties for bridge inspection. Autom Construct 2014;39:180–94. 链接1

[86]  Torok MM, Golparvar-Fard M, Kochersberger KB. Image-based automated 3D crack detection for post-disaster building assessment. J Comput Civ Eng 2014;28(5):A4014004. 链接1

[87]  Adhikari RS, Moselhi O, Bagchi A. A study of image-based element condition index for bridge inspection. In: Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining: building the future in automation and robotics (volume 2); 2013 Aug 11– 15; Montreal, QC, Canada. International Association for Automation and Robotics in Construction; 2013. p. 868–79. 链接1

[88]  Paal SG, Jeon JS, Brilakis I, DesRoches R. Automated damage index estimation of reinforced concrete columns for post-earthquake evaluations. J Struct Eng 2015;141(9):04014228. 链接1

[89]  Yeum CM, Dyke SJ. Vision-based automated crack detection for bridge inspection. Comput Civ Infrastruct Eng 2015;30(10):759–70. 链接1

[90]  Greimann LF, Stecker JH, Kao AM, Rens KL. Inspection and rating of miter lock gates. J Perform Constr Facil 1991;5(4):226–38. 链接1

[91]  Jahanshahi MR, Chen FC, Joffe C, Masri SF. Vision-based quantitative assessment of microcracks on reactor internal components of nuclear power plants. Struct Infrastruct Eng 2017;13(8):1013–26. 链接1

[92]  Ghanta S, Karp T, Lee S. Wavelet domain detection of rust in steel bridge images. In: Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing; 2011 May 22–27; Prague, Czech Republic. Piscataway: IEEE; 2011. p. 1033–6. 链接1

[93]  Jahanshahi MR, Masri SF. Parametric performance evaluation of waveletbased corrosion detection algorithms for condition assessment of civil infrastructure systems. J Comput Civ Eng 2013;27(4):345–57. 链接1

[94]  Medeiros FNS, Ramalho GLB, Bento MP, Medeiros LCL. On the evaluation of texture and color features for nondestructive corrosion detection. EURASIP J Adv Signal Process 2010;2010:817473. 链接1

[95]  Shen HK, Chen PH, Chang LM. Automated steel bridge coating rust defect recognition method based on color and texture feature. Autom Construct 2013;31:338–56. 链接1

[96]  Son H, Hwang N, Kim C, Kim C. Rapid and automated determination of rusted surface areas of a steel bridge for robotic maintenance systems. Autom Construct 2014;42:13–24. 链接1

[97]  Igoe D, Parisi AV. Characterization of the corrosion of iron using a smartphone camera. Instrum Sci Technol 2016;44(2):139–47. 链接1

[98]  Ahuja SK, Shukla MK. A survey of computer vision based corrosion detection approaches. In: Satapathy S, Joshi A, editors. Information and communication technology for intelligent systems (ICTIS 2017)—volume 2. Cham: Springer; 2018. p. 55–63. 链接1

[99]  Koch C, Brilakis I. Pothole detection in asphalt pavement images. Adv Eng Inform 2011;25(3):507–15. 链接1

[100]  Salman M, Mathavan S, Kamal K, Rahman M. Pavement crack detection using the Gabor filter. In: Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems; 2013 Oct 6–9; The Hague, the Netherlands. Piscataway: IEEE; 2013. p. 2039–44. 链接1

[101]  Hu Y, Zhao C. A novel LBP based methods for pavement crack detection. J Pattern Recognit Res 2010;5(1):140–7. 链接1

[102]  Zou Q, Cao Y, Li Q, Mao Q, Wang S. CrackTree: automatic crack detection from pavement images. Pattern Recognit Lett 2012;33(3):227–38. 链接1

[103]  Kirschke KR, Velinsky SA. Histogram-based approach for automated pavement-crack sensing. J Transp Eng 1992;118(5):700–10. 链接1

[104]  Li L, Sun L, Tan S, Ning G. An efficient way in image preprocessing for pavement crack images. In: Proceedings of the 12th COTA International Conference of Transportation Professionals; 2012 Aug 3–6; Beijing, China. Reston: American Society of Civil Engineers; 2012. p. 3095–103. 链接1

[105]  Adu-Gyamfi YO, Okine NOA, Garateguy G, Carrillo R, Arce GR. Multiresolution information mining for pavement crack image analysis. J Comput Civ Eng 2012;26(6):741–9. 链接1

[106]  Chen YL, Jahanshahi MR, Manjunatha P, Gan WP, Abdelbarr M, Masri SF, et al. Inexpensive multimodal sensor fusion system for autonomous data acquisition of road surface conditions. IEEE Sens J 2016;16(21):7731–43. 链接1

[107]  Zakeri H, Nejad FM, Fahimifar A. Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch Comput Methods Eng 2017;24(4):935–77. 链接1

[108]  Koch C, Georgieva K, Kasireddy V, Akinci B, Fieguth P. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv Eng Inform 2015;29(2):196–210. Corrigendum in: Adv Eng Inform 2016;30(2):208–10. 链接1

[109]  Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations 2015; 2015 May 7–9; San Diego, CA, USA; 2015. p. 1–14. 链接1

[110]  Xu Y, Bao Y, Chen J, Zuo W, Li H. Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Struct Heal Monit. Epub 2018 Apr 2.

[111]  Zhang L, Yang F, Zhang YD, Zhu YJ. Road crack detection using deep convolutional neural network. In: Proceedings of 2016 IEEE International Conference on Image Processing; 2016 Sep 25–28; Phoenix, AZ, USA. Piscataway: IEEE; 2016. p. 3708–12. 链接1

[112]  Cha YJ, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Comput Civ Infrastruct Eng 2017;32 (5):361–78. 链接1

[113]  Kim H, Ahn E, Shin M, Sim SH. Crack and noncrack classification from concrete surface images using machine learning. Struct Health Monit. Epub 2018 Apr 23.

[114]  Kim B, Cho S. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors (Basel) 2018;18(10):3452. 链接1

[115]  Kim B, Cho S. Automated and practical vision-based crack detection of concrete structures using deep learning. Comput Civ Infrastruct Eng. In press.

[116]  Lee Y, Kim B, Cho S. Image-based spalling detection of concrete structures incorporating deep learning. In: Proceedings of the 7th World Conference on Structural Control and Monitoring; 2018 Jul 22–25; Qingdao, China; 2018.

[117]  Atha DJ, Jahanshahi MR. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Struct Health Monit 2018;17(5):1110–28. 链接1

[118]  Chen FC, Jahanshahi MR. NB-CNN: deep learning-based crack detection using convolutional neural network and naïve Bayes data fusion. IEEE Trans Ind Electron 2018;65(5):4392–400. 链接1

[119]  Yeum CM. Computer vision-based structural assessment exploiting large volumes of images [dissertation]. West Lafayette: Purdue University; 2016. 链接1

[120]  Xu Y, Li S, Zhang D, Jin Y, Zhang F, Li N, et al. Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images. Struct Contr Health Monit 2018;25(2):e2075. 链接1

[121]  Maguire M, Dorafshan S, Thomas RJ. SDNET2018: a concrete crack image dataset for machine learning applications [dataset]. Logan: Utah State University; 2018. 链接1

[122]  Bao Y, Tang Z, Li H, Zhang Y. Computer vision and deep learning-based data anomaly detection method for structural health monitoring. Struct Heal Monit 2019;18(2):401–21. 链接1

[123]  Dang J, Shrestha A, Haruta D, Tabata Y, Chun P, Okubo K. Site verification tests for UAV bridge inspection and damage image detection based on deep learning. In: Proceedings of the 7th World Conference on Structural Control and Monitoring; 2018 Jul 22–25; Qingdao, China; 2018.

[124]  Yeum CM, Dyke SJ, Ramirez J. Visual data classification in post-event building reconnaissance. Eng Struct 2018;155:16–24. 链接1

[125]  Cha YJ, Choi W, Suh G, Mahmoudkhani S, Büyüköztürk O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput Civ Infrastruct Eng 2018;33(9):731–47. 链接1

[126]  Zhang A, Wang KCP, Li B, Yang E, Dai X, Peng Y, et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput Civ Infrastruct Eng 2017;32(10):805–19. 链接1

[127]  Hoskere V, Narazaki Y, Hoang TA, Spencer BF Jr. Vision-based structural inspection using multiscale deep convolutional neural networks. In: Proceedings of the 3rd Huixian International Forum on Earthquake Engineering for Young Researchers; 2017 Aug 11–12; Urbana, IL, USA; 2017.

[128]  Hoskere V, Narazaki Y, Hoang TA, Spencer BF Jr. Towards automated postearthquake inspections with deep learning-based condition-aware models. In: Proceedings of the 7th World Conference on Structural Control and Monitoring; 2018 Jul 22–25; Qingdao, China; 2018.

[129]  Seitz SM, Curless B, Diebel J, Scharstein D, Szeliski R. A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: volume 1; 2006 Jun 17–22; New York City, NY, USA. Piscataway: IEEE; 2006. p. 519–28. 链接1

[130]  Khaloo A, Lattanzi D, Cunningham K, Dell’Andrea R, Riley M. Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling. Struct Infrastruct Eng 2018;14(1):124–36. 链接1

[131]  Khaloo A, Lattanzi D, Jachimowicz A, Devaney C. Utilizing UAV and 3D computer vision for visual inspection of a large gravity dam. Front Built Environ 2018;4:31. 链接1

[132]  Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 1992;14(2):239–56. 链接1

[133]  Meshlab.net [Internet]. [cited 2018 Jun 20]. Available from: http://www. meshlab.net/.

[134]  CloudCompare: 3D point cloud and mesh processing software open source project [Internet]. [cited 2018 Jun 20]. Available from: http://www.danielgm. net/cc/.

[135]  Girardeau-Montaut D, Roux M, Marc R, Thibault G. Change detection on points cloud data acquired with a ground laser scanner. In: Proceedings of the ISPRS Workshop—Laser Scanning 2005; 2005 Sep 12–14; Enschede, the Netherlands; 2005. p. 30–5.

[136]  Lague D, Brodu N, Leroux J. Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z). ISPRS J Photogramm Remote Sens 2013;82:10–26. 链接1

[137]  Morgenthal G, Hallermann N. Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures. Adv Struct Eng 2014;17 (3):289–302. 链接1

[138]  Hallermann N, Morgenthal G, Rodehorst V. Unmanned aerial systems (UAS)— case studies of vision based monitoring of ageing structures. In: Proceedings of the International Symposium Non-Destructive Testing in Civil Engineering; 2015 Sep 15–17; Berlin, Germany; 2015.

[139]  Khaloo A, Lattanzi D. Automatic detection of structural deficiencies using 4D hue-assisted analysis of color point clouds. In: Pakzad S, editor. Dynamics of civil structures, volume 2. Cham: Springer; 2019. p. 197–205. 链接1

[140]  Jafari B, Khaloo A, Lattanzi D. Deformation tracking in 3D point clouds via statistical sampling of direct cloud-to-cloud distances. J Nondestruct Eval 2017;36:65. 链接1

[141]  Ghahremani K, Khaloo A, Mohamadi S, Lattanzi D. Damage detection and finite-element model updating of structural components through point cloud analysis. J Aerosp Eng 2018;31(5):04018068. 链接1

[142]  Radke RJ, Andra S, Al-Kofahi O, Roysam B. Image change detection algorithms: a systematic survey. IEEE Trans Image Process 2005;14 (3):294–307. 链接1

[143]  Hussain M, Chen D, Cheng A, Wei H, Stanley D. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 2013;80:91–106. 链接1

[144]  Tewkesbury AP, Comber AJ, Tate NJ, Lamb A, Fisher PF. A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens Environ 2015;160:1–14. 链接1

[145]  Sakurada K, Okatani T, Deguchi K. Detecting changes in 3D structure of a scene from multi-view images captured by a vehicle-mounted camera. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition; 2013 Jun 23–28; Portland, OR, USA. Piscataway: IEEE; 2013. p. 137–44. 链接1

[146]  Alcantarilla PF, Stent S, Ros G, Arroyo R, Gherardi R. Street-view change detection with deconvolutional networks. In: Proceedings of 2016 Robotics: Science and Systems Conference; 2016 Jun 18–22; Ann Arbor, MI, USA; 2016.

[147]  Stent S, Gherardi R, Stenger B, Soga K, Cipolla R. Visual change detection on tunnel linings. Mach Vis Appl 2016;27(3):319–30. 链接1

[148]  Kiziltas S, Akinci B, Ergen E, Tang P, Gordon C. Technological assessment and process implications of field data capture technologies for construction and facility/infrastructure management. Electron J Inf Technol Constr 2008;13:134–54. 链接1

[149]  Golparvar-Fard M, Peña-Mora F, Savarese S. D4AR—a 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication. J Inf Technol Constr 2009;14:129–53. 链接1

[150]  Huber D. ARIA: the Aerial Robotic Infrastructure Analyst. SPIE Newsroom [Internet] 2014 Jun 9 [cited 2019 Feb 24]. Available from: http://spie.org/ newsroom/5472-aria-the-aerial-robotic-infrastructure-analyst?SSO=1.

[151]  Earthsense.co [Internet]. Champaign: EarthSense, Inc.; [cited 2019 Feb 25]. Available from: https://www.earthsense.co.

[152]  Zhu Z, Brilakis I. Concrete column recognition in images and videos. J Comput Civ Eng 2010;24(6):478–87. 链接1

[153]  Koch C, Paal SG, Rashidi A, Zhu Z, König M, Brilakis I. Achievements and challenges in machine vision-based inspection of large concrete structures. Adv Struct Eng 2014;17(3):303–18. 链接1

[154]  Xiong X, Adan A, Akinci B, Huber D. Automatic creation of semantically rich 3D building models from laser scanner data. Autom Construct 2013;31:325–37. 链接1

[155]  Perez-Perez Y, Golparvar-Fard M, El-Rayes K. Semantic and geometric labeling for enhanced 3D point cloud segmentation. In: Perdomo-Rivera JL, Gonzáles-Quevedo A, del Puerto CL, Maldonado-Fortunet F, Molina-Bas OI, editors. Construction Research Congress 2016: old and new; 2016 May 31– June 2; San Juan, Puerto Rico. Reston: American Society of Civil Engineers; 2016. p. 2542–52. 链接1

[156]  Armeni I, Sener O, Zamir AR, Jiang H, Brilakis I, Fischer M, et al. 3D semantic parsing of large-scale indoor spaces. In: In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 1534–43. 链接1

[157]  Golparvar-Fard M, Bohn J, Teizer J, Savarese S, Peña-Mora F. Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques. Autom Construct 2011;20(8):1143–55. 链接1

[158]  Golparvar-Fard M, Peña-Mora F, Savarese S. Monitoring changes of 3D building elements from unordered photo collections. In: Proceedings of 2011 IEEE International Conference on Computer Vision Workshops; 2011 Nov 6– 13; Barcelona, Spain. Piscataway: IEEE; 2011. p. 249–56. 链接1

[159]  Lu R, Brilakis I, Middleton CR. Detection of structural components in point clouds of existing RC bridges. Comput Civ Infrastruct Eng 2019;34 (3):191–212. 链接1

[160]  Yeum CM, Choi J, Dyke SJ. Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure. Struct Health Monit. Epub 2018 Mar 27.

[161]  Gao Y, Mosalam KM. Deep transfer learning for image-based structural damage recognition. Comput Civ Infrastruct Eng 2018;33(9):748–68. 链接1

[162]  Liang X. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization. Comput Civ Infrastruct Eng. Epub 2018 December 5.

[163]  Narazaki Y, Hoskere V, Hoang TA, Spencer BF Jr. Vision-based automated bridge component recognition integrated with high-level scene understanding. In: Proceedings of the 13th International Workshop on Advanced Smart Materials and Smart Structures Technology; 2017 Jul 22–23; Tokyo, Japan; 2017.

[164]  Narazaki Y, Hoskere V, Hoang TA, Spencer BF Jr. Vision-based automated bridge component recognition with high-level scene consistency. Comput Civ Infrastruct Eng. In Press.

[165]  Narazaki Y, Hoskere V, Hoang TA, Spencer BF. Automated vision-based bridge component extraction using multiscale convolutional neural networks. In: Proceedings of the 3rd Huixian International Forum on Earthquake Engineering for Young Researchers; 2017 Aug 11–12; Urbana, IL, USA; 2017.

[166]  German S, Jeon JS, Zhu Z, Bearman C, Brilakis I, DesRoches R, et al. Machine vision-enhanced postearthquake inspection. J Comput Civ Eng 2013;27 (6):622–34. 链接1

[167]  German S, Brilakis I, Desroches R. Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for postearthquake safety assessments. Adv Eng Informatics 2012;26(4):846–58. 链接1

[168]  Anil EB, Akinci B, Garrett JH, Kurc O. Information requirements for earthquake damage assessment of structural walls. Adv Eng Inform 2016;30(1):54–64. 链接1

[169]  Anil EB, Akinci B, Kurc O, Garrett JH. Building-information-modeling–based earthquake damage assessment for reinforced concrete walls. J Comput Civ Eng 2016;30(4):04015076. 链接1

[170]  Wei Y, Kasireddy V, Akinci B, Smith I, Domer B. 3D imaging in construction and infrastructure management: technological assessment and future research directions3D imaging in construction and infrastructure management: technological assessment and future research directions. In: Advanced computing strategies for engineering: proceedings of the 25th EGICE International Workshop 2018; 2018 Jun 10–13; Lausanne, Switzerland. Cham: Springer; 2018. p. 37–60. 链接1

[171]  Farrar CR, James III GH. System identification from ambient vibration measurements on a bridge. J Sound Vibrat 1997;205(1):1–18. 链接1

[172]  Lynch JP, Sundararajan A, Law KH, Kiremidjian AS, Carryer E, Sohn H. Field validation of a wireless structural monitoring system on the Alamosa Canyon Bridge. In: Liu SC, editor. Proceedings volume 5057: Smart Structures and Materials 2003: smart systems and nondestructive evaluation for civil infrastructures; 2003 Mar 2–6; San Diego, CA, USA; 2003.

[173]  Jin G, Sain MK, Spencer Jr BF. Frequency domain system identification for controlled civil engineering structures. IEEE Trans Contr Syst Technol 2005;13(6):1055–62. 链接1

[174]  Jang S, Jo H, Cho S, Mechitov K, Rice JA, Sim SH, et al. Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation. Smart Struct Syst 2010;6(5–6):439–59. 链接1

[175]  Rice JA, Mechitov K, Sim SH, Nagayama T, Jang S, Kim R, et al. Flexible smart sensor framework for autonomous structural health monitoring. Smart Struct Syst 2010;6(5–6):423–38. 链接1

[176]  Moreu F, Kim R, Lei S, Diaz-Fañas G, Dai F, Cho S, et al. Campaign monitoring railroad bridges using wireless smart sensors networks. In: Proceedings of 2014 Engineering Mechanics Institute Conference [Internet]; 2014 Aug 5–8; Hamilton, OT, Canada, 2014 [cited 2017 Oct 11]. Available from: http:// emi2014.mcmaster.ca/index.php/emi2014/EMI2014/paper/viewPaper/139. 链接1

[177]  Cho S, Giles RK, Spencer Jr BF. System identification of a historic swing truss bridge using a wireless sensor network employing orientation correction. Struct Contr Health Monit 2015;22(2):255–72. 链接1

[178]  Sim SH, Spencer Jr BF, Zhang M, Xie H. Automated decentralized modal analysis using smart sensors. Struct Contr Health Monit 2010;17(8):872–94. 链接1

[179]  Horn BKP, Schunck BG. Determining optical flow. Artif Intell 1981;17(1– 3):185–203. 链接1

[180]  Bruhn A, Weickert J, Schnörr C. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vis 2005;61 (3):211–31. 链接1

[181]  Liu C. Beyond pixels: exploring new representations and applications for motion analysis [dissertation]. Cambridge: Massachusetts Institute of Technology; 2009. 链接1

[182]  Baker S, Scharstein D, Lewis JP, Roth S, Black MJ, Szeliski R. A database and evaluation methodology for optical flow. Int J Comput Vis 2011;92(1):1–31. 链接1

[183]  Sutton MA, Orteu JJ, Schreier HW. Image correlation for shape, motion and deformation measurements: basic concepts, theory and applications. New York City: Springer Science & Business Media; 2009. 链接1

[184]  The VIC-2DTM system [Internet]. Irmo: Correlated Solutions, Inc.; [cited 2019 Feb 24]. Available from: https://www.correlatedsolutions.com/vic-2d/.

[185]  GOM Correlate: evaluation software for 3D testing [Interent]. Braunschweig: GOM GmbH; [cited 2019 Feb 24]. Available from: https://www.gom.com/3dsoftware/gom-correlate.html.

[186]  Pan B, Qian K, Xie H, Asundi A. Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Meas Sci Technol 2009;20(6):062001. 链接1

[187]  Hoult NA, Take WA, Lee C, Dutton M. Experimental accuracy of two dimensional strain measurements using digital image correlation. Eng Struct 2013;46:718–26. 链接1

[188]  Dutton M, Take WA, Hoult NA. Curvature monitoring of beams using digital image correlation. J Bridge Eng 2014;19(3):05013001. 链接1

[189]  Ellenberg A, Branco L, Krick A, Bartoli I, Kontsos A. Use of unmanned aerial vehicle for quantitative infrastructure evaluation. J Infrastruct Syst 2015;21 (3):04014054. 链接1

[190]  McCormick N, Lord J. Digital image correlation for structural measurements. Proc Inst Civ Eng-Civ Eng 2012;165(4):185–90. 链接1

[191]  Yoneyama S, Kitagawa A, Iwata S, Tani K, Kikuta H. Bridge deflection measurement using digital image correlation. Exp Tech 2007;31(1):34–40. 链接1

[192]  Yoneyama S, Ueda H. Bridge deflection measurement using digital image correlation with camera movement correction. Mater Trans 2012;53 (2):285–90. 链接1

[193]  Helfrick MN, Niezrecki C, Avitabile P, Schmidt T. 3D digital image correlation methods for full-field vibration measurement. Mech Syst Signal Process 2011;25(3):917–27. 链接1

[194]  Reagan D. Unmanned aerial vehicle measurement using three-dimensional digital image correlation to perform bridge structural health monitoring [dissertation]. Lowell: University of Massachusetts Lowell; 2017. 链接1

[195]  Mahal M, Blanksvärd T, Täljsten B, Sas G. Using digital image correlation to evaluate fatigue behavior of strengthened reinforced concrete beams. Eng Struct 2015;105:277–88. 链接1

[196]  Ghorbani R, Matta F, Sutton MA. Full-field deformation measurement and crack mapping on confined masonry walls using digital image correlation. Exp Mech 2015;55(1):227–43. 链接1

[197]  Tomasi C, Kanade T. Detection and tracking of point features. Technical report. Pittsburgh: Carnegie Mellon University; 1991 Apr. Report No.: CMUCS-91-132.

[198]  Lewis JP. Fast normalized cross-correlation. Vis. Interface 1995;10:120–3. 链接1

[199]  Ye XW, Dong CZ, Liu T. A review of machine vision-based structural health monitoring: methodologies and applications. J Sens 2016;2016:7103039. 链接1

[200]  Feng D, Feng MQ. Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection—a review. Eng Struct 2018;156:105–17. 链接1

[201]  Nogueira FMA, Barbosa FS, Barra LPS. Evaluation of structural natural frequencies using image processing. In: Proceedings of 2005 International Conference on Experimental Vibration Analysis for Civil Engineering Structures; 2005 Oct 26–28; Bordeaux, France; 2005.

[202]  Chang CC, Xiao XH. An integrated visual-inertial technique for structural displacement and velocity measurement. Smart Struct Syst 2010;6 (9):1025–39. 链接1

[203]  Fukuda Y, Feng MQ, Narita Y, Kaneko S, Tanaka T. Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm. IEEE Sens J 2013;13(12):4725–32. 链接1

[204]  Min JH, Gelo NJ, Jo H. Real-time image processing for non-contact monitoring of dynamic displacements using smartphone technologies. In: Lynch JP, editor. Proceedings volume 9803: sensors and smart structures technologies for civil, mechanical, and aerospace systems 2016; 2016 Mar 20–24; Las Vegas, NV, USA; 2016. p. 98031B.

[205]  Dong CZ, Ye XW, Jin T. Identification of structural dynamic characteristics based on machine vision technology. Measurement 2018;126:405–16. 链接1

[206]  Celik O, Dong CZ, Catbas FN. Measurement of human loads using computer vision. In: Pakzad S, editor. Dynamics of civil structures, volume 2: proceedings of the 36th IMAC, a conference and exposition on structural dynamics 2018; 2018 Feb 12–15; Orlando, FL, USA. Cham: Springer; 2019. p. 191–5. 链接1

[207]  Lee J, Lee KC, Cho S, Sim SH. Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges. Sensors (Basel) 2017;17(10):2317. 链接1

[208]  Park JW, Moon DS, Yoon H, Gomez F, Spencer Jr BF, Kim JR. Visual-inertial displacement sensing using data fusion of vision-based displacement with acceleration. Struct Contr Health Monit 2018;25(3):e2122. 链接1

[209]  Schumacher T, Shariati A. Monitoring of structures and mechanical systems using virtual visual sensors for video analysis: fundamental concept and proof of feasibility. Sensors (Basel) 2013;13(12):16551–64. 链接1

[210]  Yoon H, Elanwar H, Choi H, Golparvar-Fard M, Spencer Jr BF. Target-free approach for vision-based structural system identification using consumergrade cameras. Struct Contr Health Monit 2016;23(12):1405–16. 链接1

[211]  Ye XW, Yi TH, Dong CZ, Liu T, Bai H. Multi-point displacement monitoring of bridges using a vision-based approach. Wind Struct 2015;20(2):315–26. 链接1

[212]  Abdelbarr M, Chen YL, Jahanshahi MR, Masri SF, Shen WM, Qidwai UA. 3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor. Smart Mater Struct 2017;26 (12):125016. 链接1

[213]  Ye XW, Yi TH, Dong CZ, Liu T. Vision-based structural displacement measurement: system performance evaluation and influence factor analysis. Measurement 2016;88:372–84. 链接1

[214]  Feng D, Feng MQ. Vision-based multipoint displacement measurement for structural health monitoring. Struct Contr Health Monit 2016;23(5):876–90. 链接1

[215]  Yoon H, Hoskere V, Park JW, Spencer BF Jr. Cross-correlation-based structural system identification using unmanned aerial vehicles. Sensors (Basel) 2017;17(9):2075. 链接1

[216]  Chen JG, Wadhwa N, Cha YJ, Durand F, Freeman WT, Buyukozturk O. Modal identification of simple structures with high-speed video using motion magnification. J Sound Vibrat 2015;345:58–71. 链接1

[217]  Cha YJ, Chen JG, Büyüköztürk O. Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters. Eng Struct 2017;132:300–13. 链接1

[218]  Yang Y, Dorn C, Mancini T, Talken Z, Kenyon G, Farrar C, et al. Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mech Syst Signal Process 2017;85:567–90. 链接1

[219]  Yang Y, Dorn C, Mancini T, Talken Z, Kenyon G, Farrar C, et al. Spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations. Struct Contr Health Monit 2018;25(8):e2193. 链接1

[220]  Zaurin R, Khuc T, Catbas FN. Hybrid sensor-camera monitoring for damage detection: case study of a real bridge. J Bridge Eng 2016;21(6):05016002. 链接1

[221]  Pan B, Tian L, Song X. Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation. NDT E Int 2016;79:73–80. 链接1

[222]  Xu Y, Brownjohn J, Kong D. A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge. Struct Contr Health Monit 2018;25(5):e2155. 链接1

[223]  Kim SW, Kim NS. Dynamic characteristics of suspension bridge hanger cables using digital image processing. NDT E Int 2013;59:25–33. 链接1

[224]  Chen JG. Video camera-based vibration measurement of infrastructure [dissertation]. Cambridge: Massachusetts Institute of Technology; 2016. 链接1

[225]  Yoon H. Enabling smart city resilience : post-disaster response and structural health monitoring [dissertation]. Urbana: University of Illinois at UrbanaChampaign; 2016. 链接1

[226]  Mas D, Ferrer B, Acevedo P, Espinosa J. Methods and algorithms for videobased multi-point frequency measuring and mapping. Measurement 2016;85:164–74. 链接1

[227]  Chen Z, Li H, Bao Y, Li N, Jin Y. Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology. Struct Contr Health Monit 2016;23(3):517–34. 链接1

[228]  Hoskere V, Park JW, Yoon H, Spencer BF Jr. Vision-based modal survey of civil infrastructure using unmanned aerial vehicles. J Struct Eng. In press.

[229]  Ros G, Sellart L, Materzynska J, Vazquez D, Lopez AM. The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA. Piscataway: IEEE; 2016. p. 3234–43. 链接1

[230]  Blender.org [Internet]. Amsterdam: Stichting Blender Foundation; [cited 2018 Aug 1]. Available from: https://www.blender.org/.

[231]  Hoskere V, Narazaki Y, Spencer BF Jr, Smith MD. Deep learning-based damage detection of miter gates using synthetic imagery from computer graphics. In: Proceedings of the 12th International Workshop on Structural Health Monitoring; 2019 Sep 10–12; Stanford, CA, USA; 2019. In press.

[232]  Hoskere V, Narazaki Y, Spencer BF Jr. Learning to detect important visual changes for structural inspections using physics-based graphics models. In: 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure; 2019 Aug 4–7; Louis, MO, USA; 2019. In press.

[233]  Narazaki Y, Hoskere V, Hoang TA, Spencer BF Jr. Automated bridge component recognition using video data. In: Proceedings of the 7th World Conference on Structural Control and Monitoring; 2018 Jul 22–25; Qingdao, China; 2018.

[234]  Shi X, Chen Z, Wang H, Yeung DY, Wong W, Woo W. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems: volume 1; 2015 Dec 7–12; Montreal, QC, Canada. Cambridge: MIT Press; 2015. p. 802–10. 链接1

[235]  Unity3d.com [Internet]. San Francisco: Unity Technologies; c2019 [cited 2019 Feb 24]. Available from: https://unity3d.com/.