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Automated Detection of Rust Defects from 3D Point Cloud Data Through Machine Learning

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Advances in Design Engineering IV (INGEGRAF 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

We introduce a method for automatic corrosion detection based on the application of machine learning techniques to 3D point cloud data generated by a LIDAR sensor. In our approach a point is assigned one of the considered class labels (healthy, stain, weld or rust) by processing its feature vector with a cascade of three binary classifiers. The effectiveness of the proposed system is demonstrated through a case study on three different bulkheads in the hold of a merchant ship. The experimental results show that the corrosion detection rate is improved by combining colour and local geometry features.

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Acknowledgements

The authors are grateful to Javier Pamies, from Ghenova, for providing us with the data on which this study is based. This work was partially supported by human resources grant RYC2020-029193-I funded by MCIN/AEI/10.13039/ 501100011033 and FSE “El FSE invierte en tu futuro”, by grant ED431F 2022/08 funded by Xunta de Galicia, Spain-GAIN, and by the project PID2021-123475 OAI00 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. The authors gratefully acknowledge CESGA (Supercomputing Center of Galicia) for providing the necessary computing resources for the development of this work. The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. CINTECX, Universidade de Vigo, GeoTech Group, 36310, Vigo, Spain

    Antonio Fernández, Cristina Pernas & Lucía Díaz-Vilariño

  2. NTNU, Department of Computer Science Ametyst-bygget, A226, 2815, Gjøvik, Norway

    Marcos X. Álvarez

Authors
  1. Antonio Fernández
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  2. Cristina Pernas
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  3. Marcos X. Álvarez
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  4. Lucía Díaz-Vilariño
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Corresponding author

Correspondence to Antonio Fernández .

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Editors and Affiliations

  1. Departamento de Ingeniería Geográfica y Técnicas de Expresión Gráfica, Universidad de Cantabria, Santander, Cantabria, Spain

    Cristina Manchado del Val

  2. Departamento de Ingeniería Mecánica y Diseño Industrial, Universidad de Cádiz, Cádiz, Spain

    Miguel Suffo Pino

  3. Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza, Zaragoza, Spain

    Ramón Miralbes Buil

  4. Departamento de Ingeniería Mecánica y Diseño Industrial, Universidad de Cádiz, Cádiz, Spain

    Daniel Moreno Sánchez

  5. Departamento de Ingeniería Mecánica y Diseño Industrial, Universidad de Cádiz, Cádiz, Spain

    Daniel Moreno Nieto

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Fernández, A., Pernas, C., Álvarez, M.X., Díaz-Vilariño, L. (2024). Automated Detection of Rust Defects from 3D Point Cloud Data Through Machine Learning. In: Manchado del Val, C., Suffo Pino, M., Miralbes Buil, R., Moreno Sánchez, D., Moreno Nieto, D. (eds) Advances in Design Engineering IV. INGEGRAF 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-51623-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-51623-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51622-1

  • Online ISBN: 978-3-031-51623-8

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