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Erschienen in: Russian Journal of Nondestructive Testing 1/2021

01.01.2021 | OPTICAL METHODS

Detection and Segmentation of Cracks in Weld Images Using ANFIS Classification Method

verfasst von: L. Mohana Sundari, P. Sivakumar

Erschienen in: Russian Journal of Nondestructive Testing | Ausgabe 1/2021

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Abstract

This paper proposes the detection and classifications of weld images for crack detection using image processing techniques. The proposed method consists of preprocessing stage, feature extraction stage, classification stage and crack region segmentation regions. The image enhancement method is used as preprocessing stage and texture and statistical features are extracted from the enhanced weld images. These computed features are then classified into “Excess weld”, “Good weld”, “No weld” and “Undercut weld”, using Adaptive Neuro Fuzzy Inference System (ANFIS) classification method. This proposed method is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value and precision. The simulation results of the proposed method are compared with other state of the art methods.

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Literatur
1.
Zurück zum Zitat Sizyakin, R., Voronin, V., Gapon, N., Zelensky, A., and Pižurica, A., Automatic detection of welding defects using the convolutional neural network, Proc. SPIE, 2019, vol. 11061. Sizyakin, R., Voronin, V., Gapon, N., Zelensky, A., and Pižurica, A., Automatic detection of welding defects using the convolutional neural network, Proc. SPIE, 2019, vol. 11061.
2.
Zurück zum Zitat Broberg, P., Surface crack detection in welds using thermography, NDT&E Int., 2013, vol. 57, pp. 69–73.CrossRef Broberg, P., Surface crack detection in welds using thermography, NDT&E Int., 2013, vol. 57, pp. 69–73.CrossRef
3.
Zurück zum Zitat Liu, Z., Lu, G., Liu, X., Jiang, X., and Lodewijks, G., Image processing algorithms for crack detection in welded structures via pulsed eddy current thermal imaging, IEEE Instrum. Meas. Mag., 2017, vol. 20, no. 4, pp. 34–44.CrossRef Liu, Z., Lu, G., Liu, X., Jiang, X., and Lodewijks, G., Image processing algorithms for crack detection in welded structures via pulsed eddy current thermal imaging, IEEE Instrum. Meas. Mag., 2017, vol. 20, no. 4, pp. 34–44.CrossRef
4.
Zurück zum Zitat Wang, G., Tse, P.W., and Yuan, M., Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector, Meas. Sci. Technol., 2018, vol. 29, art. ID 025403.CrossRef Wang, G., Tse, P.W., and Yuan, M., Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector, Meas. Sci. Technol., 2018, vol. 29, art. ID 025403.CrossRef
5.
Zurück zum Zitat Pan, M., He, Y., and Chen, L., Eddy Current Thermography Nondestructive Testing, Beijing: National Defense Industry Press, 2013, pp. 24–26. Pan, M., He, Y., and Chen, L., Eddy Current Thermography Nondestructive Testing, Beijing: National Defense Industry Press, 2013, pp. 24–26.
6.
Zurück zum Zitat Shi, Q. and Wu, K., Image segmentation algorithm for wheel set measuring based on region growing, Proc. SPIE, 2011, vol. 8200. Shi, Q. and Wu, K., Image segmentation algorithm for wheel set measuring based on region growing, Proc. SPIE, 2011, vol. 8200.
7.
Zurück zum Zitat Adhikari, R.S., Moselhi, O., and Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection, Autom. Constr., 2014, vol. 39, pp. 180–194.CrossRef Adhikari, R.S., Moselhi, O., and Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection, Autom. Constr., 2014, vol. 39, pp. 180–194.CrossRef
8.
Zurück zum Zitat Alam, S.Y., Loukili, A., Grondin, F., and Rozière, E., Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete, Eng. Fract. Mech., 2015, vol. 143, pp. 17–31.CrossRef Alam, S.Y., Loukili, A., Grondin, F., and Rozière, E., Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete, Eng. Fract. Mech., 2015, vol. 143, pp. 17–31.CrossRef
9.
Zurück zum Zitat Iyer, S. and Sinha, S.K., A robust approach for automatic detection and segmentation of cracks in underground pipeline images, Image Vision Comput., 2005, vol. 23, no. 10, pp. 931–933.CrossRef Iyer, S. and Sinha, S.K., A robust approach for automatic detection and segmentation of cracks in underground pipeline images, Image Vision Comput., 2005, vol. 23, no. 10, pp. 931–933.CrossRef
10.
Zurück zum Zitat Salman, M., Mathavan, S., Kamal, K., and Rahman, M., Pavement crack detection using the Gabor filter, Proc. 16th Int. IEEE Annual Conf. on Intelligent Transportation Systems, Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, pp. 2039–2044. Salman, M., Mathavan, S., Kamal, K., and Rahman, M., Pavement crack detection using the Gabor filter, Proc. 16th Int. IEEE Annual Conf. on Intelligent Transportation Systems, Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, pp. 2039–2044.
11.
Zurück zum Zitat Guo, W., Qu, H., and Liang, L., WDXI: The dataset of X-ray image for weld defects, Proc. 14th Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, 2018, pp. 1051–1055. Guo, W., Qu, H., and Liang, L., WDXI: The dataset of X-ray image for weld defects, Proc. 14th Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, 2018, pp. 1051–1055.
12.
Zurück zum Zitat Mohana, A. and Poobal, S., Crack detection using image processing: A critical review and analysis, Alexandria Eng. J., 2018, vol. 57, no. 2, pp. 787–798.CrossRef Mohana, A. and Poobal, S., Crack detection using image processing: A critical review and analysis, Alexandria Eng. J., 2018, vol. 57, no. 2, pp. 787–798.CrossRef
13.
Zurück zum Zitat Wu, Y., et al., Weld crack detection based on region electromagnetic sensing thermography, IEEE Sens. J., 2019, vol. 19, no. 2, pp. 751–762.CrossRef Wu, Y., et al., Weld crack detection based on region electromagnetic sensing thermography, IEEE Sens. J., 2019, vol. 19, no. 2, pp. 751–762.CrossRef
14.
Zurück zum Zitat Zhao, J., Gao, B., Woo, W.L., Qiu, F., and Tian, G.Y., Crack evaluation based on novel circle-ferrite induction thermography, IEEE Sens. J., 2017, vol. 17, no. 17, pp. 5637–5645.CrossRef Zhao, J., Gao, B., Woo, W.L., Qiu, F., and Tian, G.Y., Crack evaluation based on novel circle-ferrite induction thermography, IEEE Sens. J., 2017, vol. 17, no. 17, pp. 5637–5645.CrossRef
15.
Zurück zum Zitat Bhattad, N.M. and Patil, S.S., BR Patent 1872/MUM/2013, 2015. Bhattad, N.M. and Patil, S.S., BR Patent 1872/MUM/2013, 2015.
16.
Zurück zum Zitat Yang, R., He, Y., Gao, B., Tian, G.Y., and Peng, J., Lateral heat conduction based eddy current thermography for detection of parallel cracks and rail tread oblique cracks, Measurement, 2015, vol. 66, pp. 54–61.CrossRef Yang, R., He, Y., Gao, B., Tian, G.Y., and Peng, J., Lateral heat conduction based eddy current thermography for detection of parallel cracks and rail tread oblique cracks, Measurement, 2015, vol. 66, pp. 54–61.CrossRef
17.
Zurück zum Zitat He, Y., Tian, G.Y., Pan, M., Chen, D., and Zhang, H., An investigation into eddy current pulsed thermography for detection of corrosion blister, Corros. Sci., 2014, vol. 78, pp. 1–6.CrossRef He, Y., Tian, G.Y., Pan, M., Chen, D., and Zhang, H., An investigation into eddy current pulsed thermography for detection of corrosion blister, Corros. Sci., 2014, vol. 78, pp. 1–6.CrossRef
18.
Zurück zum Zitat Liu, Z., Lu, G., Liu, X., Jiang, X., and Lodewijks, G., Image processing algorithms for crack detection in welded structures via pulsed eddy current thermal imaging, IEEE Instrum. Meas. Mag., 2017, vol. 20 no. 4, pp. 34–44.CrossRef Liu, Z., Lu, G., Liu, X., Jiang, X., and Lodewijks, G., Image processing algorithms for crack detection in welded structures via pulsed eddy current thermal imaging, IEEE Instrum. Meas. Mag., 2017, vol. 20 no. 4, pp. 34–44.CrossRef
19.
Zurück zum Zitat Xu, C., Xie, J., Chen, G., and Huang, W., An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface, Infrared Phys. Technol., 2014, vol. 67 no. 4, pp. 266–272.CrossRef Xu, C., Xie, J., Chen, G., and Huang, W., An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface, Infrared Phys. Technol., 2014, vol. 67 no. 4, pp. 266–272.CrossRef
20.
Zurück zum Zitat Zhang, Y., The design of glass crack detection system based on image pre-processing technology, Proc. 2014 IEEE 7th Joint Int. Information Technology and Artificial Intelligence Conf. (ITAIC 2014), Piscataway, NJ: Inst. Electr. Electron. Eng., 2014, pp. 39–42. Zhang, Y., The design of glass crack detection system based on image pre-processing technology, Proc. 2014 IEEE 7th Joint Int. Information Technology and Artificial Intelligence Conf. (ITAIC 2014), Piscataway, NJ: Inst. Electr. Electron. Eng., 2014, pp. 39–42.
21.
Zurück zum Zitat Salman, M., Mathavan, S., Kamal, K., and Rahman, M., Pavement crack detection using the Gabor filter, Proc. 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC 2013), Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, pp. 2039–2044. Salman, M., Mathavan, S., Kamal, K., and Rahman, M., Pavement crack detection using the Gabor filter, Proc. 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC 2013), Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, pp. 2039–2044.
22.
Zurück zum Zitat Wang, P. and Huang, H., Comparison analysis on present image-based crack detection methods in concrete structures, Proc. 2010 3rd Int. Congr. on Image and Signal Processing, Piscataway, NJ: Inst. Electr. Electron. Eng., 2010, vol. 5, pp. 2530–2533. Wang, P. and Huang, H., Comparison analysis on present image-based crack detection methods in concrete structures, Proc. 2010 3rd Int. Congr. on Image and Signal Processing, Piscataway, NJ: Inst. Electr. Electron. Eng., 2010, vol. 5, pp. 2530–2533.
23.
Zurück zum Zitat Melin, P. and Castillo, O., Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators, Int. J. Hybrid Intell. Syst., 2014, vol. 11, no. 3, pp. 1–10. Melin, P. and Castillo, O., Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators, Int. J. Hybrid Intell. Syst., 2014, vol. 11, no. 3, pp. 1–10.
24.
Zurück zum Zitat Soto, J., Melin, P., and Castillo, O., A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators, Proc. 2013 IEEE Conf. on Computational Intelligence for Financial Engineering and Economics (CIFEr), Singapore, 2013, pp. 68–73. Soto, J., Melin, P., and Castillo, O., A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators, Proc. 2013 IEEE Conf. on Computational Intelligence for Financial Engineering and Economics (CIFEr), Singapore, 2013, pp. 68–73.
25.
Zurück zum Zitat Aguilar, L., Melin, P., and Castillo, O., Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach, Appl. Soft Comput., 2003, vol. 3, no. 3, pp. 209–219.CrossRef Aguilar, L., Melin, P., and Castillo, O., Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach, Appl. Soft Comput., 2003, vol. 3, no. 3, pp. 209–219.CrossRef
26.
Zurück zum Zitat Castillo, O. and Melin, P., Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach, Appl. Soft Comput., 2003, vol. 3, no. 4, pp. 363-378.CrossRef Castillo, O. and Melin, P., Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach, Appl. Soft Comput., 2003, vol. 3, no. 4, pp. 363-378.CrossRef
27.
Zurück zum Zitat Melin, P. and Castillo, O., Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory, Appl. Soft Comput., 20032003, vol. 3, no. 4, pp. 353–362. Melin, P. and Castillo, O., Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory, Appl. Soft Comput., 20032003, vol. 3, no. 4, pp. 353–362.
28.
Zurück zum Zitat Zhu, H., Xu, Y., Cheng, Y., et al., Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly area, China, Front. Earth Sci., 2019, vol. 13, pp. 641–655.CrossRef Zhu, H., Xu, Y., Cheng, Y., et al., Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly area, China, Front. Earth Sci., 2019, vol. 13, pp. 641–655.CrossRef
29.
Zurück zum Zitat Deotale, N.T. and Sarode, T.K., Fabric defect detection adopting combined GLCM, Gabor Wavelet features and random decision forest, 3D Res., 2019, vol. 10, p. 5. Deotale, N.T. and Sarode, T.K., Fabric defect detection adopting combined GLCM, Gabor Wavelet features and random decision forest, 3D Res., 2019, vol. 10, p. 5.
30.
Zurück zum Zitat Pandiselvi, T. and Maheswaran, R., Efficient framework for identifying, locating, detecting and classifying MRI brain tumor in MRI images, J. Med. Syst., 2019, vol. 43, p. 189.CrossRef Pandiselvi, T. and Maheswaran, R., Efficient framework for identifying, locating, detecting and classifying MRI brain tumor in MRI images, J. Med. Syst., 2019, vol. 43, p. 189.CrossRef
31.
Zurück zum Zitat Sainudiin, R. and Teng, G., Minimum distance histograms with universal performance guarantees, Jpn. J. Stat. Data Sci., 2019, vol. 2, pp. 507–527.CrossRef Sainudiin, R. and Teng, G., Minimum distance histograms with universal performance guarantees, Jpn. J. Stat. Data Sci., 2019, vol. 2, pp. 507–527.CrossRef
32.
Zurück zum Zitat Campos, G., Mastelini, S., Aguiar, G., et al., Machine learning hyperparameter selection for contrast limited adaptive histogram equalization, EURASIP J. Image Video Process., 2019, vol. 2019, p. 59.CrossRef Campos, G., Mastelini, S., Aguiar, G., et al., Machine learning hyperparameter selection for contrast limited adaptive histogram equalization, EURASIP J. Image Video Process., 2019, vol. 2019, p. 59.CrossRef
33.
Zurück zum Zitat Li, J., Hou, W., Han, Y., and Yin, J., Crack detection in tread area based on analysis of multi-scale singular area, in Computer Vision, Commun. Comput. Inf. Sci. vol. 547, Zha, H., Chen, X., Wang, L., and Miao, Q., Eds., Berlin: Springer, 2015. Li, J., Hou, W., Han, Y., and Yin, J., Crack detection in tread area based on analysis of multi-scale singular area, in Computer Vision, Commun. Comput. Inf. Sci. vol. 547, Zha, H., Chen, X., Wang, L., and Miao, Q., Eds., Berlin: Springer, 2015.
34.
Zurück zum Zitat Zhu, Y., Liu, W.-Y., Yuan, Y., Liu, F.-C., and Wang, J.-J., A defect extraction and segmentation method for radial tire X-ray image, J. Optoelectron. Laser, 2010, vol. 21, no. 5, pp. 758–761. Zhu, Y., Liu, W.-Y., Yuan, Y., Liu, F.-C., and Wang, J.-J., A defect extraction and segmentation method for radial tire X-ray image, J. Optoelectron. Laser, 2010, vol. 21, no. 5, pp. 758–761.
35.
Zurück zum Zitat Ahn, B., Choi, D.-G., Park, J., and Kweon, I.S., Real-time head pose estimation using multi-task deep neural network, Rob. Auton. Syst., 2018, vol. 103, pp. 1–12.CrossRef Ahn, B., Choi, D.-G., Park, J., and Kweon, I.S., Real-time head pose estimation using multi-task deep neural network, Rob. Auton. Syst., 2018, vol. 103, pp. 1–12.CrossRef
36.
Zurück zum Zitat Baniukiewicz, P., Automated defect recognition and identification in digital radiography, J. Nondestr. Eval., 2014, vol. 33, no. 3, pp. 327–334.CrossRef Baniukiewicz, P., Automated defect recognition and identification in digital radiography, J. Nondestr. Eval., 2014, vol. 33, no. 3, pp. 327–334.CrossRef
37.
Zurück zum Zitat Feng, S., Zhou, H., and Dong, H., Using deep neural network with small dataset to predict material defects, Mater. Des., 2019, vol. 162, pp. 300–310.CrossRef Feng, S., Zhou, H., and Dong, H., Using deep neural network with small dataset to predict material defects, Mater. Des., 2019, vol. 162, pp. 300–310.CrossRef
38.
Zurück zum Zitat Hou, W., Wei, Y., Guo, J., and Jin, Y., Automatic detection of welding defects using deep neural network, J. Phys.: Conf. Ser., 2018, vol. 933, art. ID 012006. Hou, W., Wei, Y., Guo, J., and Jin, Y., Automatic detection of welding defects using deep neural network, J. Phys.: Conf. Ser., 2018, vol. 933, art. ID 012006.
39.
Zurück zum Zitat Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, Commun. ACM, 2017, vol. 60, no. 6, pp. 84–90.CrossRef Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, Commun. ACM, 2017, vol. 60, no. 6, pp. 84–90.CrossRef
40.
Zurück zum Zitat Liao, T.W., Improving the accuracy of computer-aided radiographic weld inspection by feature selection, NDT&E Int., 2009, vol. 42, no. 4, pp. 229–239.CrossRef Liao, T.W., Improving the accuracy of computer-aided radiographic weld inspection by feature selection, NDT&E Int., 2009, vol. 42, no. 4, pp. 229–239.CrossRef
Metadaten
Titel
Detection and Segmentation of Cracks in Weld Images Using ANFIS Classification Method
verfasst von
L. Mohana Sundari
P. Sivakumar
Publikationsdatum
01.01.2021
Verlag
Pleiades Publishing
Erschienen in
Russian Journal of Nondestructive Testing / Ausgabe 1/2021
Print ISSN: 1061-8309
Elektronische ISSN: 1608-3385
DOI
https://doi.org/10.1134/S1061830921300033

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