Development of a method for quantitative assessment of paint coatings damage using computer image recognition

Laptev A.B., Matishov G.G., Shcherbakova A.V., Lapshin I.G., Gubaidullin I.M.
UDC
620.197.6:004.93
DOI
10.18577/2307-6046-2025-0-12-137-148
Article PDF (Russian) (762.75 KB)
How to cite
Laptev A.B., Matishov G.G., Shcherbakova A.V., Lapshin I.G., Gubaidullin I.M. Development of a method for quantitative assessment of paint coatings damage using computer image recognition // Proceedings of VIAM. 2025. No. 12. DOI: 10.18577/2307-6046-2025-0-12-137-148. URL: https://test.viam.ru/en/journal/2025/12/12
Keywords
paintwork, computer vision, corrosion, rust, assessment of corrosion damage, steel, neural network
Abstract

The issues of improving the accuracy of visual corrosion monitoring are relevant and are not yet fully resolved. The main approaches to the quantitative assessment of corrosion damage to metal under paint coatings have been developed. The methods involving photography with a resolution over 30 megapixels and training the program on samples both without and with visible corrosion foci using modern computer vision technologies, neural network architectures and image processing methods will make it possible to automate and improve the accuracy of quantitative assessment of paint coatings damage.

Reference list
  1. Krivushina A.A., Kogan A.M., Startsev V.O. Preservation of the properties of paint coatings after exposure to climatic factors and micro-mycetes-destructors. Trudy VIAM, 2024, no. 7 (137), paper no. 07. Available at: http://www.viam-works.ru (accessed: May 20, 2025). DOI: 10.18577/2307-6046-2024-0-7-66-76.
  2. Kablov E.N., Startsev O.V., Medvedev I.M., Panin S.V. Corrosive aggressiveness of the coastal atmosphere. Part 1. Influencing factors (review). Korroziya: materialy, zashchita, 2013, no. 12, pp. 6–18.
  3. Choi K.Y., Kim S.S. Morphological analysis and classification of types of surface corrosion damage by digital image processing. Corrosion Science, 2005, vol. 47, no. 1, pp. 1–15.
  4. Pidaparti R.M., Aghazadeh B.S., Whitfield A. et al. Classification of corrosion defects in NiAl bronze through image analysis. Corrosion Science, 2010, vol. 52, no. 11, pp. 3661–3666.
  5. Codaro E.N., Nakazato R.Z., Horovistiz A.L. et al. An image processing method for morphology characterization and pitting corrosion evaluation. Materials Science and Engineering: A, 2002, vol. 334, pp. 298–306.
  6. Codaro E.N., Nakazato R.Z., Ribeiro R.B. An image analysis study of pit formation on Ti–6Al–4V. Materials Science and Engineering: A, 2003, vol. 341, pp. 202–210.
  7. Tao L., Song S., Zhang X. et al. Image analysis of atmospheric corrosion of field exposure high strength aluminium alloys. Applied Surface Science, 2008, vol. 254, no. 21, pp. 6870–6874.
  8. Wang S., Song S. Image analysis of atmospheric corrosion exposure of zinc. Materials Science and Engineering: A, 2004, vol. 385, no. 1–2, pp. 377–381.
  9. Vinogradov S.S., Terkulova Yu.A., Kurdyukova E.A., Nikiforov A.A. Wear-proof, antifriction and fretting-resistant coating based on Ni–B. Trudy VIAM, 2015, no. 1, paper no. 02. Available at: http://www.viam-works.ru (accessed: May 01, 2025). DOI: 10.18577/2307-6046-2015-0-1-2-2.
  10. Kablov E.N. New generation materials and digital technologies for their processing. Vestnik RAN, 2020, vol. 90, no. 4, pp. 331–334.
  11. Kablov E.N., Bakradze M.M., Gromov V.I., Voznesenskaya N.M., Yakusheva N.A. New high strength structural and corrosion-resistant steels for aerospace equipment developed by FSUE «VIAM» (review). Aviacionnye materialy i tehnologii, 2020, no. 1 (58), pp. 3–11. DOI: 10.18577/2071-9140-2020-0-1-3-11.
  12. Jamaludin A.A., Imran S., Mahadi Md.A. et al. Computer Vision and Image Processing Approaches for Corrosion Detection. Journal of Marine Science and Engineering, 2023, vol. 11, аrt. 1954. DOI: 10.3390/jmse11101954.
  13. Aijazi A.K., Malaterre L., Tazir M.L. et al. Detecting and analysing corrosion spots on the hull of large marine vessels using colored 3D lidar point clouds. International Society for Photogrammetry and Remote Sensing, 2016, vol. 1, pp. 153–160.
  14. Petricca L., Moss T., Figueroa G., Broen S. Corrosion Detection Using A.I: A Comparison of Standard Computer Vision Techniques and Deep Learning Model. Computer Science & Information Technology, 2016, vol. 6, pp. 91–99. DOI: 10.5121/csit.2016.60608.
  15. Idris S.A., Jafar F.A., Jamaludin Z., Blar N. Improvement of corrosion detection using vision system for pipeline inspection. Applied Mechanics and Materials, 2015, vol. 761, аrt. 1.
  16. Ortiz A., Bonnin-Pascual F., Garcia-Fidalgo E. Visual inspection of vessels by means of a micro-aerial vehicle: an artificial neural network approach for corrosion detection. Robot 2015: Second Iberian Robotics Conference. Heidelberg: Springer, 2016, 320 р.
  17. Igoe D., Parisi A.V. Characterization of the corrosion of iron using a smartphone camera. Instruments Science Technology, 2016, vol. 44 (2), pp. 139–147. DOI: 10.1080/10739149.2015.1082484.
  18. Son H., Hwang N., Kim C. Rapid and automated determination of rusted surface areas of a steel bridge for robotic maintenance systems. Automation in Construction, 2014, vol. 42, pp. 13–24. DOI: 10.1016/j.autcon.2014.02.016.
  19. Alkanhal T.A. Image processing techniques applied for pitting corrosion analysis. International Journal of Research in Engineering and Technology, 2014, vol. 3 (1), рр. 315–336.
  20. Grengg C., Müller B., Staudinger C. et al. High-resolution optical pH imaging of concrete exposed to chemically corrosive environments. Cement and Concrete Research, 2019, vol. 116, pp. 231–237.
  21. Laptev A.B., Kogan A.M., Nikolaev E.V. et al. Mathematical modeling of the processes of production and aging of polymer composite materials. Izvestiya Natsionalnoy akademii nauk Belarusi. Seriya fiziko-tekhnicheskikh nauk, 2024, vol. 69, no. 1, pp. 65–75. DOI: 10.29235/1561-8358-2024-69-1-65-75.
  22. Startsev V.O., Kutsevich K.E., Khrulev K.A., Molokov M.V. Prediction of the surface temperature of samples of composite materials based on adhesive prepregs when exposed to climatic conditions. Klei. Germetiki. Tekhnologii, 2017, no. 9, pp. 24–31.
  23. Laptev A.B., Zakirova L.I., Zagorskikh O.A., Pavlov M.R., Gorbovets M.A. Methods of investigation of the processes of corrosion-mechanical destruction and hydrogenation of metals (review). Part 2. Formation of passive films and hydrogen sulfide cracking of steels. Trudy VIAM, 2022, no. 5 (111), paper no. 12. Available at: http://www.viam-works.ru (accessed: May 20, 2025). DOI: 10.18577/2307-6046-2022-0-5-138-146.
  24. Gorbovets M.A., Nikolaev E.V., Startsev V.O. Climate testing of Arctic materials. Part 1. Requirements for materials. Aviation materials and technologies, 2024, no. 4 (77), paper no. 12. Available at: http://www.journal.viam.ru (accessed: May 20, 2025). DOI: 10.18577/2713-0193-2024-0-4-180-188.
  25. Gorbovets M.A., Nikolaev E.V., Startsev V.O. Climate testing of Arctic materials. Part 2. Test requirements. Aviation materials and technologies, 2025, no. 1 (78), paper no. 12. Available at: http://www.journal.viam.ru (accessed: May 20, 2025). DOI: 10.18577/2713-0193-2025-0-1-138-147.