Evaluation of Nested U-Net models performance on MVTec AD dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146825" target="_blank" >RIV/00216305:26220/22:PU146825 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943348" target="_blank" >http://dx.doi.org/10.1109/ICUMT57764.2022.9943348</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943348" target="_blank" >10.1109/ICUMT57764.2022.9943348</a>

Result language

angličtina

Original language name

Evaluation of Nested U-Net models performance on MVTec AD dataset

Original language description

Anomaly detection (AD) from image data using convolutional neural networks and deep learning has become a widespread topic among both scientists and engineers. In addition to the development of new methods and models, specialized datasets are created as well. The most cited dataset specialised on anomaly detection tasks and created for testing the most recent methods is MVTec AD. This dataset has been used in more than 40 articles which are mainly devoted to creating or modifying AD methods. Subsequently, their performance is usually tested on the MVTec AD dataset. However, despite a large number of different methods and models, there is a lack of performance evaluation of U-Net++ (Nested U-Net architecture), a robust model which is well-known in the field of segmentation tasks. This article is focused on the evaluation of two Nested U-Net architectures (U-Net++, ANU-Net) on the MVTec AD dataset. It is shown that the direct use of the Nested U-Net models to reconstruct anomaly-free input data together with their strong augmentation during training phase leads to inability to reconstruct image data with anomalies at inference time. Achieved results can compete with some of the state-of-the-art reconstruction-based methods. The average image-level AUROC performance of U-Net++ model is 97.9% and 96.2% for image size of 64×64 and 128×128 pixels, respectively. Further, the average performance of ANU-Net on image-level detection is 96.5% and 96.8% for image size of 64×64 and 128x128 pixels, respectively.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20205 - Automation and control systems

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

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e-ISSN

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Number of pages

6

Pages from-to

70-75

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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