Deployment of deep learning-based anomaly detection systems: challenges and solutions

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorJežek, Štěpán
dc.contributor.authorBurget, Radim
dc.date.accessioned2024-07-09T07:47:51Z
dc.date.available2024-07-09T07:47:51Z
dc.date.issued2024cs
dc.description.abstractVisual 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.en
dc.formattextcs
dc.format.extent207-211cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 207-211. ISBN 978-80-214-6230-4cs
dc.identifier.doi10.13164/eeict.2024.207
dc.identifier.isbn978-80-214-6230-4
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249316
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectdeep learningen
dc.subjectdefect detectionen
dc.subjectsystem designen
dc.subjectalgorithm deploymenten
dc.subjectimage processingen
dc.subjectdistributed systemsen
dc.titleDeployment of deep learning-based anomaly detection systems: challenges and solutionsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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