Anomaly detection for real-world industrial applications: benchmarking recent self-supervised and pretrained methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146889" target="_blank" >RIV/00216305:26220/22:PU146889 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9943437" target="_blank" >https://ieeexplore.ieee.org/document/9943437</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943437" target="_blank" >10.1109/ICUMT57764.2022.9943437</a>

Result language

angličtina

Original language name

Anomaly detection for real-world industrial applications: benchmarking recent self-supervised and pretrained methods

Original language description

Visual anomaly detection (AD) is currently a very active research area with great potential in many real-world applications, e.g. quality control in industry and manufacturing, where it can provide cost savings and overall better product quality. Recently, many new anomaly detection methods have been introduced and many of them are intended for industrial usage. These methods are usually evaluated on a narrow selection of datasets that may differ significantly from certain types of real-world applications. Due to this approach, some methods provide different performance when deployed in real use cases. In this paper, we perform evaluation of recent state of the art visual anomaly detection methods on the problem of defect detection in metal parts fabrication, an area not well covered in existing publications. We introduce a new dataset focused specifically on the problem of metal parts fabrication and use the dataset to perform the evaluation. For the evaluation, we selected methods that use two different feature extraction approaches for anomaly detection. One of the approaches is using feature extractors pretrained on the ImageNet dataset and the second approach is training the feature extractor from scratch using self-supervised learning. We show that, in contrast to one of the most widely used anomaly detection benchmark - the MVTec-AD dataset, self-supervised methods perform significantly better (in average by 17 % AUROC) on the proposed dataset.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20204 - Robotics and automatic control

Project

<a href="/en/project/FW03010273" target="_blank" >FW03010273: Defectoscopy of painted parts using automatic adaptation of neural networks</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9866-3

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

64-69

Publisher name

IEEE

Place of publication

Valencia, Spain

Event location

Valencia, Spain

Event date

Oct 11, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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