Neural Networks For Visual Classification And Inspection Of The Industrial Products
but.event.date | 23.04.2020 | cs |
but.event.title | Student EEICT 2020 | cs |
dc.contributor.author | Míček, Vojtěch | |
dc.date.accessioned | 2021-07-15T11:17:21Z | |
dc.date.available | 2021-07-15T11:17:21Z | |
dc.date.issued | 2020 | cs |
dc.description.abstract | The aim of this thesis is to enable evaluation of quality, or the type of product in industrial applications using artificial neural networks, especially in applications where the classical approach of machine vision is too complicated. The system thus designed is implemented onto a specific hardware platform and becomes a subject to the final optimalisation for the hardware platform for the best performance of the system. | en |
dc.format | text | cs |
dc.format.extent | 245-248 | cs |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers. s. 245-248. ISBN 978-80-214-5867-3 | cs |
dc.identifier.isbn | 978-80-214-5867-3 | |
dc.identifier.uri | http://hdl.handle.net/11012/200570 | |
dc.language.iso | cs | cs |
dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
dc.relation.ispartof | Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers | en |
dc.relation.uri | https://conf.feec.vutbr.cz/eeict/EEICT2020 | cs |
dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
dc.rights.access | openAccess | en |
dc.subject | Neural networks | en |
dc.subject | machine vision | en |
dc.subject | product inspection | en |
dc.subject | object classification | en |
dc.subject | Nvidia Jetson Xavier | en |
dc.subject | Raspberry Pi | en |
dc.subject | Intel Movidius | en |
dc.subject | OpenCV | en |
dc.subject | Keras | en |
dc.subject | TensorFlow | en |
dc.title | Neural Networks For Visual Classification And Inspection Of The Industrial Products | en |
dc.type.driver | conferenceObject | en |
dc.type.status | Peer-reviewed | en |
dc.type.version | publishedVersion | en |
eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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