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Image Anomaly Detection by Aggregating Deep Pyramidal Representations

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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Abstract

Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset.

This work was partially funded by Beantech srl.

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Author information

Authors and Affiliations

  1. Università degli Studi di Udine, via delle Scienze 206, 33100, Udine, Italy

    Pankaj Mishra, Claudio Piciarelli & Gian Luca Foresti

Authors
  1. Pankaj Mishra
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  2. Claudio Piciarelli
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  3. Gian Luca Foresti
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Corresponding author

Correspondence to Claudio Piciarelli .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell'Informazione, University of Firenze, Florence, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Mishra, P., Piciarelli, C., Foresti, G.L. (2021). Image Anomaly Detection by Aggregating Deep Pyramidal Representations. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_51

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  • DOI: https://doi.org/10.1007/978-3-030-68799-1_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68798-4

  • Online ISBN: 978-3-030-68799-1

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