DOI QR코드

DOI QR Code

Real-time comprehensive image processing system for detecting concrete bridges crack

  • Lin, Weiguo (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Sun, Yichao (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Yang, Qiaoning (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Lin, Yaru (College of Information Science and Technology, Beijing University of Chemical Technology)
  • 투고 : 2019.01.20
  • 심사 : 2019.04.30
  • 발행 : 2019.06.25

초록

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

키워드

참고문헌

  1. Azarafza, M., Derakhshi, M.R.F. and Azarafza, M. (2017), "Computer modeling of crack propagation in concrete retaining walls: A case study", Comput. Concrete, 19(5), 509-514. https://doi.org/10.12989/cac.2017.19.5.509.
  2. Cen, J.H., Zhao, J.K. and Xia, X. (2017), "Application research on convolution neural network for bridge crack detection", 2nd International Conference on Computer Engineering, Information Science and Application Technology, Wuhan, China, July.
  3. Cord, A. and Chambon, S. (2012), "Automatic road defect detection by textural pattern recognition based on adaboost", Comput. Aid. Civil Inf., 27(4), 244-259. https://doi.org/10.1111/j.1467-8667.2011.00736.x.
  4. Fujita, Y. and Hamamoto, Y. (2011), "A robust automatic crack detection method from noisy concrete surfaces", Mach. Vision. Appl., 22(2), 245-254. https://doi.org/10.1111/j.1467-8667.2011.00736.x.
  5. Gasser, C.T. (2007), "Validation of 3D crack propagation in plain concrete. Part II: Computational modeling and predictions of the PCT3D test", Comput. Concrete, 4(1), 67-82. http://dx.doi.org/10.12989/cac.2007.4.1.067.
  6. Ghanta, S., Birken, R. and Dy, J. (2012), "Automatic road surface defect detection from grayscale images", SPIE Conference on Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure and Homeland Security, San Diego, Canada, March.
  7. Gibb, S., La, H.M. and Louis, S. (2018), "A genetic algorithm for convolutional network structure optimization for concrete crack detection", IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, July.
  8. Grazina, K., Povilas, T. and Gintautas, T. (2018), "Analysis of 2d feature spaces for deep learning-based speech recognition", J. Audio Eng. Soc., 66(12), 1072-1081. https://doi.org/10.17743/jaes.2018.0066.
  9. Huang, J.P., Liu, W.Y. and Sun, X.M. (2014), "A pavement crack detection method combining 2D with 3D information based on dempster-shafer theory", Comput. Aid. Civil Inf., 29(4), 299-313. https://doi.org/10.1111/mice.12041.
  10. Jahanshahi, M.R., Masri, S.F., Padgett, C.W. and Sukhatme, G.S. (2013), "An innovative methodology for detection and quantification of cracks through incorporation of depth perception", Mach. Vision. Appl., 24(2), 227-241. https://doi.org/10.1007/s00138-011-0394-0.
  11. Jang, K., Kim, N. and An, YK. (2018), "Deep learning-based autonomous concrete crack evaluation through hybrid image scanning", Struct. Hlth. Monit., 1475921718821719. https://doi.org/10.1177/1475921718821719.
  12. Jang, K.Y. and An, Y.K. (2018), "Multiple crack evaluation on concrete using a line laser thermography scanning system", Smart Struct. Syst., 22(2), 201-207. https://doi.org/10.12989/sss.2018.22.2.201.
  13. Kaul, V., Yezzi, A. and Tsai, Y. (2012), "Detecting curves with unknown endpoints and arbitrary topology using minimal paths", IEEE T. Pattern Anal., 34(10), 1952-65. https://doi.org/10.1109/TPAMI.2011.267
  14. Kim, H., Sim, S.H. and Cho, S. (2015), "Unmanned Aerial Vehicle (uav)-powered concrete crack detection based on digital image processing", Proceedings of the 6th International Conference on Advances in Experimental Structural Engineering, Illinois, United States, August.
  15. Kumar, S. and Barai, S.V. (2012), "Size-effect of fracture parameters for crack propagation in concrete: a comparative study", Comput. Concrete, 9(1), 1-19. https://doi.org/10.12989/cac.2012.9.1.001.
  16. Lee, B.Y., Kim, Y.Y., Yi, S.T. and Kim, J.K. (2013), "Automated image processing technique for detecting and analysing concrete surface cracks", Struct. Infrastr. E, 9(6), 567-577. https://doi.org/10.1080/15732479.2011.593891.
  17. Li, S.Y. and Zhao, X.F. (2018), "Convolutional neural networksbased crack detection for real concrete surface ", Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Denver, Canada, March.
  18. Lins, R.G. and Givigi, S.N. (2016), "Automatic crack detection and measurement based on image analysis", IEEE T. Instr. Meas., 65(3), 583-590. https://doi.org/10.1109/TIM.2015.2509278
  19. Liu, Y.F., Cho, S.J., Spencer, B.F.J. and Fan, J.S. (2014), "Automated assessment of cracks on concrete surfaces using adaptive digital image processing", Smart Struct. Syst., 14(4), 719-741. https://doi.org/10.12989/sss.2014.14.4.719.
  20. Miguel, G., David, B., Oscar, M., Llorca, D.F. and Sotelo, M.A. (2011), "Adaptive road crack detection system by pavement classification", Sensor. Basel., 11(10), 9628-9657. https://doi.org/10.3390/s111009628.
  21. Nejad, F.M. and Zakeri, H. (2011), "A comparison of multiresolution methods for detection and isolation of pavement distress", Exp. Syst. Appl., 38(3), 2857-2872. https://doi.org/10.1016/j.eswa.2010.08.079.
  22. Nhung, T.H.N., Thanh, H.L., Stuart, P. and Thi, T.N. (2018), "Pavement crack detection using convolutional neural network", ACM Association for Computing Machinery, Da Nang, Viet Nam, December.
  23. Oliveira, H. and Correia, P.L. (2013), "Automatic road crack detection and characterization", IEEE T. Intell. Transp., 14(1), 155-168. https://doi.org/10.1109/TITS.2012.2208630.
  24. Oliveira, H.J.M. and Correia, P.L.S.L. (2014), "CrackIT-an image processing toolbox for crack detection and characterization", IEEE International Conference on Image Processing, Paris, France, October.
  25. Prasanna, P., Dana, K.J., Gucunski, N., Basily, B.B., La, H.M., Lim, R.S. and Parvardeh, H. (2016), "Automated crack detection on concrete bridges", IEEE T. Autom Sci. Eng., 13(2), 591-599. https://doi.org/10.1109/TASE.2014.2354314.
  26. Radopoulou, S.C. and Brilakis, L. (2015), "Patch detection for pavement assessment", Automat. Constr., 53, 95-104. https://doi.org/10.1016/j.autcon.2015.03.010.
  27. Salari, E. and Bao, G. (2011), "Pavement distress detection and severity analysis", Proc Spie, 7877(3), 78770C-&8770C-10. https://doi.org/10.1117/12.876724.
  28. Samik, B. and Sukhendu, D. (2018), "Mutual variation of information on transfer-CNN for face recognition with degraded probe samples", Neurocomput., 310, 299-315. https://doi.org/10.1016/j.neucom.2018.05.038.
  29. Shi, Y., Cui, L.M., Qi, Z.Q. and Fan, M. (2016), "Automatic road crack detection using random structured forests", IEEE T. Intell. Transp., 17(12), 3434-3445. https://doi.org/10.1109/TITS.2016.2552248.
  30. Su, T.C. and Yang, M.D. (2018), "Morphological segmentation based on edge detection-II for automatic concrete crack measurement", Comput. Concrete, 21(6), 727-73. https://doi.org/10.12989/cac.2018.21.6.727.
  31. Tan, T., Qian, Y.M. and Hu, H. (2018), "Adaptive very deep convolutional residual network for noise robust speech recognition", IEEE-ACM T. Audio Spe., 26(8), 1393-1405. https://doi.org/10.1109/TASLP.2018.2825432.
  32. Tax, D.M.J. and Duin, R.P.W. (2004), "Support vector data description", Mach. Learn, 54(1), 45-66. https://doi.org/10.1023/B:MACH.0000008084.60811.49.
  33. Tsai, Y.C.J. and Li, F. (2012), "Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3d laser technology", J. Tran. Eng., 138(5), 649-656. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000353.
  34. Wang, P.H., Hu, Y.B., Dai, Y. and Tian, M.R. (2017), "Asphalt pavement pothole detection and segmentation based on wavelet energy field", Math. Prob. Eng., 2017(8), 1-13. https://doi.org/10.1155/2017/1604130.
  35. Yang, X.C., Li, H., Yu, Y.T., Luo, X.C. and Huang T. (2018), "Automatic pixel-level crack detection and measurement using fully convolutional network", Comput. Aid. Civil Inf., 33(12), 1090-1109. https://doi.org/10.1111/mice.12412.
  36. Ying, L. and Salari, E. (2010), "Beamlet transform-based technique for pavement crack detection and classification", Comput. Aid. Civil Inf., 25(8), 572-580. https://doi.org/10.1111/j.1467-8667.2010.00674.x.
  37. Zalama, E., Gomez-Garcia-Bermejo, J., Medina, R. and Llamas, J. (2014), "Road crack detection using visual features extracted by gabor filters", Comput. Aid. Civil Inf., 29(5), 342-358. https://doi.org/10.1111/mice.12042.
  38. Zhang, A., Wang, K.C.P., Li, B.X., Yang, E.H., Dai, X.X., Peng, Y., Fei, Y., Liu, Y., Li, J.Q. and Chen, C. (2017), "Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network", Comput. Aid. Civil Inf., 32(10), 805-819. https://doi.org/10.1111/mice.12297.
  39. Zhang, D.J., Li, Q.Q., Chen, Y., Cao, M., He, L. and Zhang, B.L. (2016), "An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection", Image. Vision Comput., 57(C), 130-146. https://doi.org/10.1016/j.imavis.2016.11.018.
  40. Zhang, J.P., Wang, F.Y., Wang, K.F., Lin, W.H., Xu, X. and Chen, C. (2011), "Data-driven intelligent transportation systems: a survey", IEEE T. Intell. Transp., 12(4), 1624-1639. https://doi.org/10.1109/TITS.2011.2158001.
  41. Zhao, G.T., Wang, T.Q. and Ye, J.Y. (2014), "Surface shape recognition method for crack detection", J. Electron. Imag., 23(3), 1267-1276. https://doi.org/10.1117/1.JEI.23.3.033013.
  42. Zhao, G.T., Wang, T.Q. and Ye, J.Y. (2015), "Anisotropic clustering on surfaces for crack extraction", Mach. Vision. Appl., 26, 675-688. https://doi.org/10.1007/s00138-015-0682-1.
  43. Zhao, Y., Noori, M. and Altabey, W.A. (2017), "Damage detection for a beam under transient excitation via three different algorithms", Struct. Eng. Mech., 64(6), 803-817. https://doi.org/10.12989/sem.2017.64.6.803.
  44. Zou, Q., Cao, Y., Li, Q.Q., Mao, Q.Z. and Wang, S. (2012), "Cracktree: automatic crack detection from pavement images", Pattern Recogn Lett., 33(3), 227-238. https://doi.org/10.1016/j.patrec.2011.11.004.
  45. Zou, Q., Zhang, Z., Li, Q., Qi, X.B., Wang, Q. and Wang, S. (2019), "Deep crack: learning hierarchical convolutional features for crack detection", IEEE T. Image Proc., 28(3), 1498-1512. https://doi.org/10.1109/TIP.2018.2878966.

피인용 문헌

  1. Crack detection based on ResNet with spatial attention vol.26, pp.5, 2019, https://doi.org/10.12989/cac.2020.26.5.411