Uncertainty Estimation for Ultrasonic Inspection of Composite Aerial Structures
Source abstract
Abstract
Components made of polymeric composite materials, such as wings and stabilizers of aircrafts, are periodically inspected using non-destructive testing methods. Ultrasonic testing is one of the primary inspection methods applied in the aircraft/aerospace industry. Image processing methods have been developed for the purpose of ultrasonic data analysis and increasing the inspection efficiency. A critically important factor in damage sizing is appropriate processing of ultrasonic data in order to extract the damage region properly before calculation of its extent. The paper presents a comparative analysis of various image segmentation methods in the light of accuracy of damage detection in ultrasonic C-Scans of composite structures. A brief review of image segmentation methods is presented and their usefulness in the ultrasonic testing applications is discussed. The selected methods, namely the threshold-, edge-, region-, and clustering-based ones, were tested using ultrasonic C-Scans of a specimen with barely visible impact damage and aircraft panels with delamination, all made of CFRP composites. A problem with the selection of appropriate input parameters using most of the segmentation methods is discussed, and non-parametric histogram-based approaches are proposed. A quantitative analysis of the accuracy of damage detection using the segmentation methods is presented and the most suitable approaches are introduced. The proposed processing procedures may significantly improve the objectivity of inspections of composite elements and structures using ultrasonic testing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
Show source-provided references
1. Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111–122 (1981)10.1016/0031-3203(81)90009-1 ↗
2. Bernsen, J.: Dynamic thresholding of grey-level images. In: Proceedings of the 8th Int. Conf. on Pattern Recognition, pp. 1251–1255 (1986)
3. Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: Proc. International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation. Rennes, France (1979)
4. Bezdek, J.C., Ehrlich, R., Full, W.: Fcm: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984). https://doi.org/10.1016/0098-3004(84)90020-710.1016/0098-3004(84)90020-7 ↗
5. Bhanu, B., Lee, S.: Genetic Learning for Adaptive Image Segmentation, vol. 287. Springer, New York (2012)
6. Bhardwaj, S., Mittal, A.: A survey on various edge detector techniques. Procedia Technol. 4(Supplement C), 220–226 (2012). https://doi.org/10.1016/j.protcy.2012.05.033 (2nd International Conference on Computer, Communication, Control and Information Technology (C3IT-2012) on February 25–26, 2012)10.1016/j.protcy.2012.05.033 ↗
7. Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007)10.1080/2151237X.2007.10129236 ↗
8. Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, Chichester (2009)10.1002/9780470744055 ↗
9. Chaki, N., Shaikh, S.H., Saeed, K.: Exploring Image Binarization Techniques. Studies in Computational Intelligence. Springer (2014)10.1007/978-81-322-1907-1 ↗
10. Chen, W.T., Wen, C.H., Yang, C.W.: A fast two-dimensional entropic thresholding algorithm. Pattern Recogn. 27(7), 885–893 (1994)10.1016/0031-3203(94)90154-6 ↗
11. De, S., Bhattacharyya, S., Chakraborty, S., Dutta, P.: Hybrid Soft Computing for Multilevel Image and Data Segmentation, Chap. 2—Image Segmentation: A Review. Computational Intelligence Methods and Applications. Springer (2016)10.1007/978-3-319-47524-0 ↗
12. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39, 1–38 (1977)10.1111/j.2517-6161.1977.tb01600.x ↗
13. Donadon, M.V., Iannucci, L., Falzon, B.G., Hodgkinson, J.M., de Almeida, S.F.M.: A progressive failure model for composite laminates subjected to low velocity impact damage. Comput. Struct. 86(11), 1232–1252 (2008). https://doi.org/10.1016/j.compstruc.2007.11.00410.1016/j.compstruc.2007.11.004 ↗
14. Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)10.1145/361237.361242 ↗
15. Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)10.1080/01969727308546046 ↗
16. Feng, M.L., Tan, Y.P.: Contrast adaptive binarization of low quality document images. IEICE Electron. Express 1(16), 501–506 (2004)10.1587/elex.1.501 ↗
17. Fu, K.S., Mui, J.: A survey on image segmentation. Pattern Recogn. 13(1), 3–16 (1981)10.1016/0031-3203(81)90028-5 ↗
18. Fu, Z., Wang, L.: Color image segmentation using gaussian mixture model and EM algorithm. In: Multimedia and Signal Processing, pp. 61–66. Springer (2012)10.1007/978-3-642-35286-7_9 ↗
19. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)10.1109/TIT.1975.1055330 ↗
20. Giurgiutiu, V.: Chapter 5—Damage and failure of aerospace composites. In: Giurgiutiu, V. (ed.) Structural Health Monitoring of Aerospace Composites, pp. 125–175. Academic Press, Oxford (2016). https://doi.org/10.1016/B978-0-12-409605-9.00005-210.1016/B978-0-12-409605-9.00005-2 ↗
21. Glasbey, C.A.: An analysis of histogram-based thresholding algorithms. CVGIP 55(6), 532–537 (1993)
22. Haralick, R.M.: Image segmentation survey. In: Faugeras, O.D. (ed.) Fundamentals in Computer Vision, pp. 209–224. Cambridge Univ Press, Cambridge (1983)
23. Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. C 28(1), 100–108 (1979)
24. Hough, P.V.C.: Method and means for recognizing complex patterns (1962). US Patent 3069654
25. Jain, A.K., Dubuisson, M.P.: Segmentation of x-ray and c-scan images of fiber reinforced composite materials. Pattern Recogn. 25(3), 257–270 (1992)10.1016/0031-3203(92)90109-V ↗
26. Kapur, J., Sahoo, P., Wong, A.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985). https://doi.org/10.1016/0734-189X(85)90125-210.1016/0734-189X(85)90125-2 ↗
27. Katunin, A.: Impact damage assessment in composite structures based on multiwavelet analysis of modal shapes. Indian J. Eng. Mater. Sci. 22, 451–459 (2015)
28. Katunin, A.: Stone impact damage identification in composite plates using modal data and quincunx wavelet analysis. Arch. Civil Mech. Eng. 15(1), 251–261 (2015). https://doi.org/10.1016/j.acme.2014.01.01010.1016/j.acme.2014.01.010 ↗
29. Kefali, A., Sari, T., Bahi, H.: Text/background separation in the degraded document images by combining several thresholding techniques. WSEAS Trans. Signal Process. 10, 436–443 (2014)
30. Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of niblack inspired binarization methods for ancient documents. DRR 7247, 1–10 (2009)
31. Kittler, J., Illingworth, J.: On threshold selection using clustering criteria. IEEE Trans. Syst. Man Cybern. 5, 652–655 (1985)10.1109/TSMC.1985.6313443 ↗
32. Kumar, M.J., Kumar, D.G.R., Reddy, R.V.K.: Review on image segmentation techniques. International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN, pp. 2278–0882 (2014)
33. Li, S., Poudel, A., Chu, T.P.: An image enhancement technique for ultrasonic NDE of CFRP panels. In: 21st Annual Research Symposium & Spring Conference 2012, pp. 21–26 (2012)
34. Liao, P.S., Chen, T.S., Chung, P.C., et al.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713–727 (2001)
35. Niblack, W.: An Introduction to Digital Image Processing. Strandberg Publishing Company, Birkeroed (1985)
36. Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)10.1109/TMI.2006.877092 ↗
37. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)10.1109/TPAMI.2004.110 ↗
38. Ohlander, R., Price, K., Reddy, D.R.: Picture segmentation using a recursive region splitting method. Comput. Graph. Image Process. 8(3), 313–333 (1978)10.1016/0146-664X(78)90060-6 ↗
39. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)10.1109/TSMC.1979.4310076 ↗
40. Pal, N.R., Pal, S.K.: Entropic thresholding. Sig. Process. 16(2), 97–108 (1989). https://doi.org/10.1016/0165-1684(89)90090-X10.1016/0165-1684(89)90090-X ↗
41. Papamarkos, N., Atsalakis, A.: Gray-level reduction using local spatial features. Comput. Vis. Image Underst. 78(3), 336–350 (2000). https://doi.org/10.1006/cviu.2000.083810.1006/cviu.2000.0838 ↗
42. Prewitt, J., Mendelsohn, M.L.: The analysis of cell images. Ann. N. Y. Acad. Sci. 128(1), 1035–1053 (1966)10.1111/j.1749-6632.1965.tb11715.x ↗
43. Pun, T.: A new method for grey-level picture thresholding using the entropy of the histogram. Sig. Process. 2(3), 223–237 (1980)10.1016/0165-1684(80)90020-1 ↗
44. Reddi, S., Rudin, S., Keshavan, H.: An optimal multiple threshold scheme for image segmentation. IEEE Trans. Syst. Man Cybern. 4, 661–665 (1984)10.1109/TSMC.1984.6313341 ↗
45. Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. 8(8), 630–632 (1978)10.1109/TSMC.1978.4310039 ↗
46. Roerdink, J.B., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Inform. 41(1,2), 187–228 (2000)10.3233/FI-2000-411207 ↗
47. Sahoo, P., Soltani, S., Wong, A.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41(2), 233–260 (1988). https://doi.org/10.1016/0734-189X(88)90022-910.1016/0734-189X(88)90022-9 ↗
48. Saini, K., Dewal, M., Rohit, M.: Ultrasound imaging and image segmentation in the area of ultrasound: a review. Int. J. Adv. Sci. Technol. 24, (2010)
49. Samanta, S., Datta, D.: Imaging of impacted composite armours using data clustering. In: Proceedings of the 18th World Conference on Nondestructive Testing. Durban, South Africa (2012)
50. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)10.1016/S0031-3203(99)00055-2 ↗
51. Sezan, M.I.: A peak detection algorithm and its application to histogram-based image data reduction. Comput. Vis. Graph. Image Process. 49(1), 36–51 (1990). https://doi.org/10.1016/0734-189X(90)90161-N10.1016/0734-189X(90)90161-N ↗
52. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)10.1117/1.1631315 ↗
53. Shaw, K., Lohrenz, M.: A survey of digital image segmentation algorithms. Tech. rep., Naval Oceanographic and Atmospheric Research Lab. Stennis Space Center, MS (1995)10.21236/ADA499374 ↗
54. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)10.1109/34.868688 ↗
55. Skarbek, W., Koschan, A.: Colour image segmentation a survey. IEEE Trans. Circuits Syst. Video Technol. 14, (1994)
56. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 4th edn. Cengage Learning, Boston (2014)
57. Sridevi, S., Sundaresan, M.: Survey of image segmentation algorithms on ultrasound medical images. In: Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on, pp. 215–220. IEEE (2013)10.1109/ICPRIME.2013.6496475 ↗
58. Stathis, P., Kavallieratou, E., Papamarkos, N.: An evaluation technique for binarization algorithms. J. UCS 14(18), 3011–3030 (2008)
59. Stefaniuk, M., Dragan, K.: Improvements to image processing algorithms used for delamination damage extraction and modeling. In: Proceedings of the 19th World Conference on Non-Destructive Testing, pp. 1–8. Munich (2016)
60. Weszka, J.S.: A survey of threshold selection techniques. Comput. Graph. Image Process. 7(2), 259–265 (1978)10.1016/0146-664X(78)90116-8 ↗
61. Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Pattern Anal. Appl. 6(4), 309–326 (2004)10.1007/s10044-003-0197-7 ↗
62. Wronkowicz, A., Dragan, K.: Damage size monitoring of composite aircraft structures based on ultrasonic testing and image processing. Compos. Theory Pract. 16(3), 154–160 (2016)
63. Wronkowicz, A., Dragan, K., Dziendzikowski, M., Chalimoniuk, M., Sbarufatti, C.: 3D reconstruction of ultrasonic B-scans for nondestructive testing of composites. In: L.J. Chmielewski, A. Datta, R. Kozera, K. Wojciechowski (eds.) Proceedings of the International Conference on Computer Vision and Graphics, pp. 266–277. Springer (2016)10.1007/978-3-319-46418-3_24 ↗
64. Wronkowicz, A., Dragan, K., Lis, K.: Assessment of uncertainty in damage evaluation by ultrasonic testing of composite structures. Compos. Struct. 203, 71–84 (2018). https://doi.org/10.1016/j.compstruct.2018.06.10910.1016/j.compstruct.2018.06.109 ↗
65. Wronkowicz, A., Katunin, A.: Damage evaluation in composite structures based on image processing of ultrasonic c-scans. Stud. Inform. 38(1B), 31–40 (2017)
66. Wronkowicz, A., Katunin, A., Dragan, K.: Ultrasonic c-scan image processing using multilevel thresholding for damage evaluation in aircraft vertical stabilizer. Int. J. Image Graph. Signal Process. 7(11), 1–8 (2015)10.5815/ijigsp.2015.11.01 ↗
67. Zack, G., Rogers, W., Latt, S.: Automatic measurement of sister chromatid exchange frequency. J. Histochem. Cytochem. 25(7), 741–753 (1977)10.1177/25.7.70454 ↗
68. Zaitoun, N.M., Aqel, M.J.: Survey on image segmentation techniques. Procedia Comput. Sci. 65, 797–806 (2015)10.1016/j.procs.2015.09.027 ↗
69. Zhu, H., Meng, F., Cai, J., Lu, S.: Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. J. Vis. Commun. Image Represent. 34, 12–27 (2016)10.1016/j.jvcir.2015.10.012 ↗