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Unsupervised Defect Detection for Infrastructure Inspection

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14404))

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Abstract

Artificial Intelligence (AI) provides a fundamental aid in building operations, allowing infrastructure inspection and compliance with safety standards. In the collaborative tasks involved, detecting areas of interest, such as surface defects, is crucial. A drawback of supervised AI-based approaches is that they require manual annotation, which entails additional costs. This paper presents a novel unsupervised anomaly detection approach for locating defects based on generative models that learn the distribution of defect-free images. Using attention maps to validate in a subset, we propose a formulation that does not require accessing labelled images, enabling task automation, maintenance optimisation and cost reduction.

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Funding

This work has received funding from Horizon Europe, the European Union’s Framework Programme for Research and Innovation, under Grant Agreement No. 101058054 (TURBO) and No. 101057404 (ZDZW). The work of Rocío del Amor has been supported by the Spanish Ministry of Universities (FPU20/05263).

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Authors and Affiliations

  1. Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain

    N. P. García-de-la-Puente, Rocío del Amor, Fernando García-Torres, Adrián Colomer & Valery Naranjo

Authors
  1. N. P. García-de-la-Puente
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  2. Rocío del Amor
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  3. Fernando García-Torres
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  4. Adrián Colomer
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  5. Valery Naranjo
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Corresponding author

Correspondence to N. P. García-de-la-Puente .

Editor information

Editors and Affiliations

  1. University of Évora, Évora, Portugal

    Paulo Quaresma

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Manchester, Manchester, UK

    Hujun Yin

  4. University of Évora, Évora, Portugal

    Teresa Gonçalves

  5. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  6. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

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García-de-la-Puente, N.P., del Amor, R., García-Torres, F., Colomer, A., Naranjo, V. (2023). Unsupervised Defect Detection for Infrastructure Inspection. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_14

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