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Erschienen in:
Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

verfasst von : Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.

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Vorheriges Kapitel Deep Learning Based Dimple Segmentation for Quantitative Fractography
Nächstes Kapitel Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines
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Metadaten
Titel
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
verfasst von
Thomas Defard
Aleksandr Setkov
Angelique Loesch
Romaric Audigier
Copyright-Jahr
2021
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_35

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