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DC poleHodnotaJazyk
dc.contributor.authorGuérand, Benoît
dc.contributor.authorScheer, Fabian
dc.contributor.authorDemetgül, Mustafa
dc.contributor.authorFleischer, Jürgen
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T10:38:55Z
dc.date.available2022-09-01T10:38:55Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 142-151.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49588
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjecthluboké učenícs
dc.subjectResnet50cs
dc.subjectRetinaNetcs
dc.subjectmezery ve dveříchcs
dc.subjectdetekce objektů otevřených klapek autacs
dc.subjectkonvoluční neuronové sítěcs
dc.subjectCNNcs
dc.subjectpřípad průmyslového použitícs
dc.subjectvýrobní linkacs
dc.titleDeep Learning for the Detection of Car Flap Statesen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn recent years, deep learning and object detection has continuously attracted more attention. Especially in the automotive world where many car manufacturers are currently investigating its possible applications. On production lines, even if processes are more and more automatized mistakes can happen and hinder the performance of an industrial plant. In this study, a method and application of object detection-based deep learning algorithm to detect open flaps on cars, like doors, trunk, hood etc. is examined. With this approach, the advantages of gap detection in cars on production lines, specifically the application of Resnet50 Convolutional Neural Networks (CNNs) and transfer learning in an industrial use case, are demonstrated. We show how the problem of detecting open flaps on cars is modeled in a way that a CNN can be applied to this new kind of application and present a detailed evaluation of the results and challenges. Finally, many suggestions are given for future applications of similar algorithms.en
dc.subject.translateddeep learningen
dc.subject.translatedResnet50en
dc.subject.translatedRetinaNeten
dc.subject.translateddoor gapsen
dc.subject.translatedobject detection of open car flapsen
dc.subject.translatedconvolutional neural networksen
dc.subject.translatedCNNen
dc.subject.translatedindustrial use caseen
dc.subject.translatedproduction lineen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.18
dc.type.statusPeer-revieweden
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