Smart Structures and Systems

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A fast image-stitching algorithm for characterization of cracks in large-scale structures

  • Wang, Linlin (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Spencer, Billie F. Jr. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Li, Junjie (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Hu, Pan (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • Received : 2020.02.11
  • Accepted : 2021.01.03
  • Published : 2021.04.25
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Abstract

Visual inspection of concrete cracks has been widely used in structural health monitoring (SHM). Capturing high-resolution images is an effective method to visualize a complete crack, but it is difficult to show a whole crack from a single high-resolution image. One feasible method is using image stitching technique to stitch several images into a complete crack map. However, the current image stitching method is a computationally intensive process. Numerous images are required to cover large-scale structures with sufficient resolution, this can be computationally prohibitive. To address this problem, an improved image stitching method for crack damage evaluation is proposed, which can quickly stitch the crack images without affecting the quality of the stitching or the resulting images. Rather than first stitching the images together and then determining the crack maps, we propose to first develop the crack maps for the individual images and then stitch them together. The proposed method reduces the number of redundant matching points between the original images by combining their characteristics during image stitching, so it can reduce the calculation time without affecting the quality. Also, the results will not be influenced by the image stitching seam, which can reduce the complexity of the algorithm. Several experimental results are provided in this article to demonstrate that the proposed method can reduce the calculation time without affecting the quality of image stitching and have better robustness than the current method in use.

Keywords

Acknowledgement

The research is supported by the National Key R & D Program of China (2016YFC0401600 and 2017YFC0404900), the National Natural Science Foundation of China (51979027, 51769033 and 51779035), the Fundamental Research Funds for the Central Universities (DUT17ZD205). We thank the China Scholarship Council for sponsoring Linlin Wang's visit to University of Illinois at Urbana-Champaign, USA. And the authors extend their sincere thanks to Yasutaka Narazaki and Vedhus Hoskere to propose some comments.

References

  1. Adhikari, R., Moselhi, O. and Bagchi, A. (2014), "Image-based retrieval of concrete crack properties for bridge inspection", Autom. Constr., 39, 180-194. https://doi.org/10.1016/j.autcon.2013.06.011
  2. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D. and Cohen, M. (2004), "Interactive digital photomontage", In: ACM SIGGRAPH 2004 Papers, August. https://doi.org/10.1145/1186562.1015718
  3. Andreaus, U., Baragatti, P., Casini, P. and Iacoviello, D. (2017), "Experimental damage evaluation of open and fatigue cracks of multi-cracked beams by using wavelet transform of static response via image analysis", Struct. Control Health Monitor., 24(4), e1902. https://doi.org/10.1002/stc.1902
  4. Attard, L., Debono, C. J., Valentino, G. and Di Castro, M. (2018). "Tunnel inspection using photogrammetric techniques and image processing: A review", ISPRS J. Photogramm. Remote Sens., 144, 180-188. https://doi.org/10.1016/j.isprsjprs.2018.07.010
  5. Bradski, G. and Kaehler, A. (2008), Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media, Inc., Sebastopol, CA, USA.
  6. Brown, M.A. (2005), "Multi-image matching using invariant features", Ph.D. Dissertation, University of British Columbia, Vancouver, Canada.
  7. Brown, M. and Lowe, D.G. (2007), "Automatic panoramic image stitching using invariant features", Int. J. Comput. Vision, 74(1), 59-73. https://doi.org/10.1007/s11263-006-0002-3
  8. Canny, J. (1986), "A computational approach to edge detection", IEEE T. Pattern Anal., 8(6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851
  9. Cao, J., Chen, L., Wang, M. and Tian, Y. (2018), "Implementing a parallel image edge detection algorithm based on the Otsucanny operator on the Hadoop platform", Comput. Intell. Neurosci., 2018. https://doi.org/10.1155/2018/3598284
  10. Cha, Y.J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput-Aided Civ. Inf., 32(5), 361-378. https://doi.org/10.1111/mice.12263
  11. Chen, C.Y. and Klette, R. (1999), "Image stitching-Comparisons and new techniques", Proceedings of International Conference on Computer Analysis of Images and Patterns, Springer, Berlin, Heidelberg, Germany, September. https://doi.org/10.1007/3-540-48375-6_73
  12. Chia, W.C., Chew, L.W., Ang, L.M. and Seng, K.P. (2012), "Low memory image stitching and compression for WMSN using strip-based processing", Int. J. Sens. Networks, 11(1), 22-32. https://doi.org/10.1504/IJSNET.2012.045037
  13. Du, C., Yuan, J., Dong, J., Li, L., Chen, M. and Li, T. (2019), "GPU based Parallel Optimization for Real Time Panoramic Video Stitching", Pattern Recogn. Lett. https://doi.org/10.1016/j.patrec.2019.06.018
  14. Fischler, M.A. and Bolles, R.C. (1981), "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography", Commun. ACM, 24(6), 381-395. https://doi.org/10.1145/358669.358692
  15. GB50010-2015 (2015), Code for design of concrete structures, Standardization administration of the people's republic of China; Beijing, China.
  16. Graybeal, B.A., Phares, B.M., Rolander, D.D., Moore, M. and Washer, G. (2002), "Visual inspection of highway bridges", J. Nondestruct. Eval., 21(3), 67-83. https://doi.org/10.1023/a:1022508121821
  17. Haralick, R.M., Sternberg, S.R. and Zhuang, X. (1987), "Image analysis using mathematical morphology", IEEE T. Pattern Anal., (4), 532-550. https://doi.org/10.1109/TPAMI.1987.4767941
  18. Hutchinson, T.C. and Chen, Z. (2006), "Improved image analysis for evaluating concrete damage", J. Comput. Civil Eng. - ASCE, 20(3), 210-216. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:3(210)
  19. Islam, M. and Kim, J.M. (2019), "Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder-decoder network", Sens., 19(19), 4251. https://doi.org/10.3390/s19194251
  20. Jahanshahi, M.R., Masri, S.F. and Sukhatme, G.S. (2011), "Multi-image stitching and scene reconstruction for evaluating defect evolution in structures", Struct. Health Monit., 10(6), 643-657. https://doi.org/10.1177/1475921710395809
  21. Jang, K. and An, Y.K. (2018), "Multiple crack evaluation on concrete using a line laser thermography scanning system", Smart Struct. Syst., Int. J., 22(2), 201-207. https://doi.org/10.12989/sss.2018.22.2.201
  22. Kim, B. and Cho, S. (2019), "Image-based concrete crack assessment using mask and region-based convolutional neural network", Struct. Control Health Monit., e2381. https://doi.org/10.1002/stc.2381
  23. Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M. and Sim, S.H. (2017), "Concrete crack identification using a UAV incorporating hybrid image processing", Sens., 17(9), 2052. https://doi.org/10.3390/s17092052
  24. La, H.M., Gucunski, N., Kee, S.H. and Van Nguyen, L. (2015), "Data analysis and visualization for the bridge deck inspection and evaluation robotic system", Visualization Eng., 3(1), 6. https://doi.org/10.1186/s40327-015-0017-3
  25. Lins, R.G. and Givigi, S.N. (2016), "Automatic crack detection and measurement based on image analysis", IEEE T. Instrum. Meas., 65(3), 583-590. https://doi.org/10.1109/TIM.2015.2509278
  26. Liu, Y., Cho, S., Spencer Jr, B.F. and Fan, J. (2014), "Automated assessment of cracks on concrete surfaces using adaptive digital image processing", Smart Struct. Syst., Int. J., 14(4), 719-741. ttp://doi.org/10.12989/sss.2014.14.4.719
  27. Lowe, D.G. (2004), "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vision, 60(2), 91-110. http://dx.doi.org/10.1023/b:visi.0000029664.99615.94
  28. Nishikawa, T., Yoshida, J., Sugiyama, T. and Fujino, Y. (2012), "Concrete crack detection by multiple sequential image filtering", Comput-Aided Civ. Inf., 27(1), 29-47. http://dx.doi.org/10.1111/j.1467-8667.2011.00716.x
  29. Osman, M.K., Mashor, M.Y., Saad, Z. and Jaafar, H. (2009), "Contrast enhancement for Ziehl-Neelsen tissue slide images using linear stretching and histogram equalization technique", Proceedings of the 2009 IEEE Symposium on Industrial Electronics & Applications, Kuala Lumpur, Malaysia, October. https://doi.org/10.1109/ISIEA.2009.5356411
  30. Pulli, K., Baksheev, A., Kornyakov, K. and Eruhimov, V. (2012), "Real-Time Computer Vision with OpenCV", Commun. ACM, 55(6), 61-69. http://dx.doi.org/10.1145/2184319.2184337
  31. Qu, Z., Ju, F.R., Guo, Y., Bai, L. and Chen, K. (2018), "Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods", PLoS One, 13(7), 0201109. https://doi.org/10.1371/journal.pone.0201109
  32. Szeliski, R. (2006), "Image Alignment and Stitching: A Tutorial", Foundations Trends® Comput. Graphics Vision, 2(1), 1-104. http://dx.doi.org/10.1561/0600000009
  33. Wagner, D. Mulloni, A., Langlotz, T. and Schmalstieg, D. (2010), "Real-time panoramic mapping and tracking on mobile phones", Proceedings of the 2010 IEEE Virtual Reality Conference, Waltham, USA, March. https://doi.org/10.1109/VR.2010.5444786
  34. Yamaguchi, T. and Hashimoto, S. (2010), "Fast crack detection method for large-size concrete surface images using percolation-based image processing", Mach. Vision Appl., 21(5), 797-809. https://doi.org/10.1007/s00138-009-0189-8
  35. Zhang, T. and Suen, C.Y. (1984), "A fast parallel algorithm for thinning digital patterns", Commun. ACM, 27(3), 236-239. https://doi.org/10.1145/357994.358023
  36. Zhu, Z., German, S. and Brilakis, I. (2010), "Detection of large-scale concrete columns for automated bridge inspection", Autom. Constr., 19(8), 1047-1055. https://doi.org/10.1016/j.autcon.2010.07.016