Pore Segmentation in Industrial Radiographic Images Using Adaptive Thresholding and Morphological Analysis

Autores

DOI:

https://doi.org/10.46420/TAES.e230008

Palavras-chave:

radiographic image segmentation, adaptive thresholding, pore detection, industrial radiography, morphological analysis

Resumo

This study presents an innovative approach to segmenting pores in low-quality industrial radiographic images using advanced image processing and adaptive segmentation techniques. The process involves several steps, including conversion of the image to grayscale to simplify analysis. Gaussian and median filters are then applied to improve image quality and enable more accurate segmentation. The methodology is based on adaptive thresholding and edge detection using the Canny algorithm, achieving accurate pore area measurements, even for those unnoticed by experts, with measurement errors of less than 0.1 mm². This high accuracy supports the method's effectiveness in pore detection and measurement. The resulting tool has great potential in industrial radiography applications, from quality control to defect analysis. While recognizing the need for continued research to improve contrast and illumination, as well as to address potential sources of error, these refinements promise to further elevate an already reliable and accurate method with a wide reach in the industry.

Referências

Aslam, Y., Santhi, N., Ramasamy, N., & Ramar, K. (2019). A modified adaptive thresholding method using cuckoo search algorithm for detecting surface defects. International Journal of Advanced Computer Science and Applications, 10(5), 214-220.

BD, R. V. (2020). Deep Learning for Quality Prediction in Dissimilar Spot Welding DP600-AISI304, Using a Convolutional Neural Network and Infrared Image Processing. European Modeling & Simulation Symposium,

Contreras, C. E., Soldara, S. M. M., Cruz, O. S., Garcia, S. B., & Zarate, J. D. (2022). SISTEMA DE VISIÓN DE DIMENSIONAMIENTO MORFOLÓGICO PARA LA COLOCACIÓN DE FRENOS CORRECTORES DENTALES (MORPHOLOGICAL SIZING VISION SYSTEM FOR THE PLACEMENT OF DENTAL CORRECTIVE BRACES). Pistas Educativas, 43(141).

Duan, F., Yin, S., Song, P., Zhang, W., Zhu, C., & Yokoi, H. (2019). Automatic welding defect detection of x-ray images by using cascade adaboost with penalty term. IEEE Access, 7, 125929-125938. https://doi.org/https://10.1109/ACCESS.2019.2927258

Dwivedi, S. K., Vishwakarma, M., & Soni, A. (2018). Advances and researches on non destructive testing: A review. Materials Today: Proceedings, 5(2), 3690-3698. https://doi.org/https://doi.org/10.1016/j.matpr.2017.11.620

Eckel, S., Zscherpel, U., Huthwaite, P., Paul, N., & Schumm, A. (2020). Radiographic film system classification and noise characterisation by a camera-based digitisation procedure. NDT & E International, 111, 102241. https://doi.org/https://doi.org/10.1016/j.ndteint.2020.102241

Golodov, V., & Maltseva, A. (2022). Approach to weld segmentation and defect classification in radiographic images of pipe welds. NDT & E International, 127, 102597. https://doi.org/https://doi.org/10.1016/j.ndteint.2021.102597

Gong, Y., Luo, J., Shao, H., & Li, Z. (2022). A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures. Composite Structures, 284, 115136. https://doi.org/https://doi.org/10.1016/j.compstruct.2021.115136

Hernández, A. E., Villarinho, L. O., Ferraresi, V. A., Orozco, M. S., Roca, A. S., & Fals, H. C. (2020). Optimization of resistance spot welding process parameters of dissimilar DP600/AISI304 joints using the infrared thermal image processing. The International Journal of Advanced Manufacturing Technology, 108, 211-221. https://doi.org/https://doi.org/10.1007/s00170-020-05374-y

Hu, A., Wu, L., Huang, J., Fan, D., & Xu, Z. (2022). Recognition of weld defects from X-ray images based on improved convolutional neural network. Multimedia Tools and Applications, 81(11), 15085-15102. https://doi.org/https://doi.org/10.1007/s11042-022-12546-3

Jonsson, B., Dobmann, G., Hobbacher, A., Kassner, M., & Marquis, G. (2016). IIW guidelines on weld quality in relationship to fatigue strength (Vol. 158). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-19198-0

León Ovelar, L. R., Cikel, K., & Gregor Recalde, D. O. (2021). Inteligencia artificial al servicio de la salud pública: caso de estudio detección temprana de focos larvarios de mosquitos. XXXVII Congreso Interamericano Virtual de Ingeniería Sanitaria y Ambiental,

Li, L., Ren, J., Wang, P., Gao, H., Sun, M., Sha, B., . . . Li, X. (2023). A pixel-level weak supervision segmentation method for typical defect images in X-ray inspection of solid rocket motors combustion chamber. Measurement, 211, 112497. https://doi.org/https://doi.org/10.1016/j.measurement.2023.112497

Li, L., Ren, J., Wang, P., Lü, Z., Li, X., & Sun, M. (2022). An adaptive false-color enhancement algorithm for super-8-bit high grayscale X-ray defect image of solid rocket engine shell. Mechanical Systems and Signal Processing, 179, 109398. https://doi.org/https://doi.org/10.1016/j.ymssp.2022.109398

Liu, B., & Yang, T. (2017). Image analysis for detection of bugholes on concrete surface. Construction and Building Materials, 137, 432-440. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2017.01.098

Liu, W., Shan, S., Chen, H., Wang, R., Sun, J., & Zhou, Z. (2022). X-ray weld defect detection based on AF-RCNN. Welding in the World, 66(6), 1165-1177. https://doi.org/https://doi.org/10.1007/s40194-022-01281-w

Niño, C., Castro Casadiego, S., Medina Delgado, B., Camargo, L., & Guevara-Ibarra, D. (2021). Comparativa entre la técnica de umbralización binaria y el método de Otsu para la detección de personas. Revista UIS ingenierías, 20(2 (2021)), 65-74. https://doi.org/https://10.18273/revuin.v20n2-2021006

Patil, R. V., Reddy, Y., & Thote, A. M. (2021). Multi-class weld defect detection and classification by support vector machine and artificial neural network. Modeling, Simulation and Optimization: Proceedings of CoMSO 2020,

Pérez de la Parte, M., Espinel Hernández, A., Sánchez Orozco, M. C., Sánchez Roca, A., Jimenez Macias, E., Blanco Fernández, J., & Carvajal Fals, H. (2022). Effect of zinc coating on delay nugget formation in dissimilar DP600-AISI304 welded joints obtained by the resistance spot welding process. The International Journal of Advanced Manufacturing Technology, 120(3-4), 1877-1887. https://doi.org/https://doi.org/10.1007/s00170-022-08849-2

Radi, D., Abo-Elsoud, M. E. A., & Khalifa, F. (2022). Accurate segmentation of weld defects with horizontal shapes. NDT & E International, 126, 102599. https://doi.org/https://doi.org/10.1016/j.ndteint.2021.102599

Rafiei, M., Raitoharju, J., & Iosifidis, A. (2023). Computer vision on x-ray data in industrial production and security applications: A comprehensive survey. Ieee Access, 11, 2445-2477.

Ríos, L., Roncaglia, G., & Ortiz de Zárate, R. (2022). Análisis y clasificación de ladrillos de hormigón celular a través de imágenes.

Truong, M. T. N., & Kim, S. (2018). Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection. Soft Computing, 22, 4197-4203. https://doi.org/https://doi.org/10.1007/s00500-017-2709-1

Tyystjärvi, T., Virkkunen, I., Fridolf, P., Rosell, A., & Barsoum, Z. (2022). Automated defect detection in digital radiography of aerospace welds using deep learning. Welding in the World, 66(4), 643-671. https://doi.org/https://doi.org/10.1007/s40194-022-01257-w

Wang, D., & Gao, W. (2021). Study of x-ray image defect detection methods for girth welds. Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering,

Wang, S., Xia, X., Ye, L., & Yang, B. (2021). Automatic detection and classification of steel surface defect using deep convolutional neural networks. Metals, 11(3), 388. https://doi.org/https://doi.org/10.3390/met11030388

Wang, X., & Yu, X. (2023). Understanding the effect of transfer learning on the automatic welding defect detection. NDT & E International, 134, 102784. https://doi.org/https://doi.org/10.1016/j.ndteint.2022.102784

Wang, Z., Lei, X., & Gao, W. (2022). Study on SDR extraction of ring weld defects of pipeline. Welding in the World, 66(8), 1645-1652. https://doi.org/https://doi.org/10.1007/s40194-022-01323-3

Yahaghi, E., Mirzapour, M., & Movafeghi, A. (2021). Comparison of traditional and adaptive multi-scale products thresholding for enhancing the radiographs of welded object. The European Physical Journal Plus, 136, 1-13.

Zhan, X., Zhang, D., Yu, H., Chen, J., Li, H., & Wei, Y. (2018). Research on X-ray image processing technology for laser welded joints of aluminum alloy. The International Journal of Advanced Manufacturing Technology, 99, 683-694. https://doi.org/https://doi.org/10.1140/epjp/s13360-021-01733-0

Zhang, B., Wang, X., Cui, J., Wu, J., Wang, X., Li, Y., . . . Wu, W. (2023). Welding defects classification by weakly supervised semantic segmentation. NDT & E International, 102899. https://doi.org/https://doi.org/10.1016/j.ndteint.2023.102899

Zhang, J., Guo, Z., Jiao, T., & Wang, M. (2018). Defect detection of aluminum alloy wheels in radiography images using adaptive threshold and morphological reconstruction. Applied Sciences, 8(12), 2365. https://doi.org/https://doi.org/10.3390/app8122365

Downloads

Publicado

2023-12-29

Edição

Volume 1 (2023)

Seção

Seção Artigos

Licença

Copyright (c) 2023 Trends in Agricultural and Environmental Sciences

Creative Commons License

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

  • You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
  • No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

This is a human-readable summary of (and not a substitute for) the license.