Deployment of deep learning-based anomaly detection systems: challenges and solutions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU152035" target="_blank" >RIV/00216305:26220/24:PU152035 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/eeict.2024.207" target="_blank" >10.13164/eeict.2024.207</a>
Alternative languages
Result language
angličtina
Original language name
Deployment of deep learning-based anomaly detection systems: challenges and solutions
Original language description
Visual anomaly detection systems play an important role in various domains, including surveillance, industrial quality control, and medical imaging. However, the deployment of such systems presents significant challenges due to a wide range of possible scene setups with varying number of devices and high computational requirements of deep learning algorithms. This research paper investigates the challenges encountered during the deployment of visual anomaly detection systems for industrial applications and proposes solutions to address them effectively. We present a model use case scenario from real-world manufacturing quality control and propose an efficient distributed system for deployment of the defect detection methods in manufacturing facilities. The proposed solution aims to provide a general framework for deploying visual defect detection algorithms base on deep neural networks and their high computational requirements. Additionally, the paper addresses challenges related the whole process of automated quality control, which can be performed with varying number of camera devices and it mostly requires interaction with other factory services or workers themselves. We believe the presented framework can contribute to more widespread use of deep learning-based defect detection systems, which may provide valuable feedback for further research and development.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings II of the 30th Student EEICT 2024: Selected Papers
ISBN
978-80-214-6230-4
ISSN
2788-1334
e-ISSN
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Number of pages
5
Pages from-to
207-211
Publisher name
Brno University of Technology, Faculty of Electronic Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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