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2021 | OriginalPaper | Buchkapitel

Image Anomaly Detection by Aggregating Deep Pyramidal Representations

verfasst von : Pankaj Mishra, Claudio Piciarelli, Gian Luca Foresti

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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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.

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Titel: Image Anomaly Detection by Aggregating Deep Pyramidal Representations
verfasst von: Pankaj Mishra
Claudio Piciarelli
Gian Luca Foresti
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68798-4

Electronic ISBN: 978-3-030-68799-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68799-1_51

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