Automatic and Accurate Determination of Defect Size in Shearography Using U-Net Deep Learning Network
Source abstract
Abstract
Abstract Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Optical measurement and interference techniques · Image and Object Detection Techniques · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
Show source-provided references
1. Wang, B., Zhong, S., Lee, T.L., et al.: Non-destructive testing and evaluation of composite materials/structures: a state-of-the-art review. Adv. Mech. Eng. 12(4), 168781402091376 (2020)10.1177/1687814020913761 ↗
2. Ormenisan, A., Szava, D.T., Szava, I., et al.: Mechanical stress exploration inside of dental filling materials by means of electronic speckle pattern interferometry/shearography. Mater. Plast 54(4), 616–619 (2017)10.37358/MP.17.4.4911 ↗
3. Farahani, B.V., Direito, F., Sousa, P.J., et al.: Electronic speckle pattern interferometry for fatigue crack monitoring. Proced. Struct. Integr. 37, 873–879 (2022)10.1016/j.prostr.2022.02.021 ↗
4. Kumar, M., Shakher, C.: Measurement of temperature and temperature distribution in gaseous flames by digital speckle pattern shearing interferometry using holographic optical element. Opt. Lasers Eng. 73, 33–39 (2015)10.1016/j.optlaseng.2015.04.002 ↗
5. Sirohi, R.: Shearography and its applications-a chronological review. Light: Adv. Manuf. 3(1), 35–64 (2022)
6. Tao, N., Anisimov, A.G., Groves, R.M.: Shearography non-destructive testing of thick GFRP laminates: numerical and experimental study on defect detection with thermal loading. Compos. Struct. 282, 115008 (2022)10.1016/j.compstruct.2021.115008 ↗
7. Angelis, G.D., Meo, M., Almond, D.P., et al.: A new technique to detect defect size and depth in composite structures using digital shearography and unconstrained optimization. NDT E Int. 45(1), 91–96 (2012)10.1016/j.ndteint.2011.07.007 ↗
8. Lv, C., Wang, K., Gu, G., et al.: Accurate full-edge detection and depth measurement of internal defects using digital speckle pattern interferometry. NDT E Int. 102, 1–8 (2019)10.1016/j.ndteint.2018.10.009 ↗
9. Gu, G., Pan, Y., Qiu, C., et al.: Improved depth characterization of internal defect using the fusion of shearography and speckle interferometry. Opt. Laser Technol. 135, 106701 (2021)10.1016/j.optlastec.2020.106701 ↗
10. Michel, F., Renotte, Y.L., Habraken, S.: Measurement of the defect size by shearography or other interferometric techniques. Opt. Eng. 51(3), 033602 (2012)10.1117/1.OE.51.3.033602 ↗
11. Liu, L., Guo, C., Wang, L., et al.: Nondestructive visualization and quantitative characterization of defects in silicone polymer insulators based on laser shearography. IEEE Sens. J. 19(15), 6508–6516 (2019)10.1109/JSEN.2019.2912269 ↗
12. Wang, K.F.: Quantitative detection of internal defects based on morphological opening, filling and binarizing operations on wrapped phase of out-of-plane deformation in digital speckle pattern interferometry. NDT E Int. 103, 173–176 (2019)10.1016/j.ndteint.2019.03.002 ↗
13. Peng, Y., Liu, G., Quan, Y., et al.: The depth measurement of internal defect based on laser speckle shearing interference. Opt. Laser Technol. 92, 69–73 (2017)10.1016/j.optlastec.2017.01.004 ↗
14. Cheng, X., Qi, H., Wu, Z., et al.: Automated detection of delamination defects in composite laminates from ultrasonic images based on object detection networks. J. Nondestr. Eval. 43(3), 94 (2024)10.1007/s10921-024-01116-2 ↗
15. He, X., Jiang, X., Mo, R., et al.: Research on ultrasonic NDT of wire to terminal joints: comparison of combinations of various CNNs and signal processing technologies. J. Nondestr. Eval. 43(3), 82 (2024)10.1007/s10921-024-01094-5 ↗
16. Liu, H., Li, W., Yang, L., et al.: Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network. Neural Comput. Appl. 34(24), 21701–21714 (2022)10.1007/s00521-022-07622-6 ↗
17. Liu, H., Wang, S., Zhao, Y., et al.: A cyclic self-enhancement technique for complex defect profile reconstruction based on thermographic evaluation. Acta Mech. Sin. 41(5), 424076 (2025)10.1007/s10409-024-24076-x ↗
18. Fotouhi, S., Pashmforoush, F., Bodaghi, M., et al.: Autonomous damage recognition in visual inspection of laminated composite structures using deep learning. Compos. Struct. 268, 113960 (2021)10.1016/j.compstruct.2021.113960 ↗
19. Zhao, W., Chen, F., Huang, H., et al.: A new steel defect detection algorithm based on deep learning. Comput. Intell. Neurosci. 2021, 1–13 (2021)10.1155/2021/5592878 ↗
20. Gurrola-Ramos, J., Dalmau, O., Alarcón, T.: U-Net based neural network for fringe pattern denoising. Opt. Lasers Eng. 149, 106829 (2022)10.1016/j.optlaseng.2021.106829 ↗
21. Wang, K., Li, Y., Kemao, Q., et al.: One-step robust deep learning phase unwrapping. Opt. Expr. 27(10), 15100 (2019)10.1364/OE.27.015100 ↗
22. Chang, C.Y., Srinivasan, K., Wang, W.C., et al.: Quality assessment of tire shearography images via ensemble hybrid faster region-based ConvNets. Electronics 9(1), 45 (2019)10.3390/electronics9010045 ↗
23. Ye, Y., Ma, K., Zhou, H., et al.: An automated shearography system for cylindrical surface inspection. Measurement 135, 400–405 (2019)10.1016/j.measurement.2018.11.085 ↗
24. de Oliveira, B.C.F., Borges, V.K., Baldo, C.R., et al.: Comparison between segmentation performances of a tool based on wavelet decomposition and multithreshold and of a U-net convolutional neural network applied to shearography images of carbon fiber reinforced plastic plates with low-velocity impact damages. Opt. Eng. 59(5), 051406 (2020)10.1117/1.OE.59.5.051406 ↗
25. Allevi, G., Pandarese, G., Revel, G.M.: Improvement of defect size and morphological estimation in shearography inspection by wavelet transform. Rev. Sci. Instrum. 90(10), 105111 (2019)10.1063/1.5093146 ↗
26. Guo, B., Zheng, X., Gerini-Romagnoli, M., et al.: Digital shearography for NDT: determination and demonstration of the size and the depth of the smallest detectable defect. NDT and E Int. 139, 102927 (2023)10.1016/j.ndteint.2023.102927 ↗
27. Aebischer, H.A., Rechsteiner, P.: Theoretical prediction of the effect of shear distortion in the Michelson interferometer. Pure Appl. Opt.: J. Eur. Opt. Soc. Part A 6(3), 303 (1997)10.1088/0963-9659/6/3/003 ↗
28. Lee, J.R., Yoon, D.J., Kim, J.S., et al.: Investigation of shear distance in Michelson interferometer-based shearography for mechanical characterization. Meas. Sci. Technol. 19(11), 115303 (2008)10.1088/0957-0233/19/11/115303 ↗
29. Zastavnik, F., Pyl, L., Gu, J., et al.: Calibration and correction procedure for quantitative out-of-plane shearography. Meas. Sci. Technol. 26(4), 045201 (2015)10.1088/0957-0233/26/4/045201 ↗
30. Wu, R., Li, Y., Lu, Y.: Single-frame temporal phase-shifting shearography for real-time nondestructive testing. Opt. Laser Technol. 176, 110972 (2024)10.1016/j.optlastec.2024.110972 ↗
31. Qin, Y., Chen, J., Fan, H.: The study and application of a new filtering method on electronic speckle pattern interferometric fringe. Opt. Lasers Eng. 39(4), 449–456 (2003)10.1016/S0143-8166(02)00027-1 ↗
32. Tounsi, Y., Kumar, M., Nassim, A., et al.: Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods. Appl. Opt. 57(27), 7681–7690 (2018)10.1364/AO.57.007681 ↗
33. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)10.1109/34.888718 ↗
34. Kumar, M., Saxena, R.: Algorithm and technique on various edge detection: a survey. Signal Image Process. 4(3), 65 (2013)
35. Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vis. Gr. Image Process. 44(1), 87–116 (1988)10.1016/S0734-189X(88)80033-1 ↗
36. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, October 5-9, 2015, proceedings, part III 18. Springer International Publishing, 2015: 234-241.10.1007/978-3-319-24574-4_28 ↗
37. Völker, A.C., Zakharov, P., Weber, B., et al.: Laser speckle imaging with an active noise reduction scheme. Opt. Exp. 13(24), 9782–9787 (2005)10.1364/OPEX.13.009782 ↗
38. Maity A, Pattanaik A, Sagnika S, et al. A comparative study on approaches to speckle noise reduction in images. 2015 International conference on computational intelligence and networks. IEEE, 2015: 148–155.10.1109/CINE.2015.36 ↗
39. Zhu, L., Zhang, C., Zhang, C., et al.: Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning. Appl. Soft Comput. 83, 105596 (2019)10.1016/j.asoc.2019.105596 ↗
40. Bardis, M., Houshyar, R., Chantaduly, C., et al.: Deep learning with limited data: organ segmentation performance by U-Net. Electronics 9(8), 1199 (2020)10.3390/electronics9081199 ↗
41. Huang, Y.H., Ng, S.P., Liu, L., et al.: NDT&E using shearography with impulsive thermal stressing and clustering phase extraction. Opt. Lasers Eng. 47(7–8), 774–781 (2009)10.1016/j.optlaseng.2009.02.011 ↗
42. Yang, F., Ye, X., Qiu, Z., et al.: The effect of loading methods and parameters on defect detection in digital shearography. Res. Phys. 7, 3744–3755 (2017)
43. Wei, Y., Ding, L., Han, Y., et al.: Characterizing defects in materials with fusion of thermography and shearography. Measurement 182, 109736 (2021)10.1016/j.measurement.2021.109736 ↗