Development and experimental evaluation of a machine learning assisted lwir polarimetric imaging system for transport anomaly detection

dc.contributor.authorPařez, Jan
dc.contributor.authorKovář, Patrik
dc.contributor.authorTater, Adam
dc.contributor.authorBallada, Ondřej
dc.contributor.authorBarta, Čestmír
dc.contributor.editorRendl, Jan
dc.date.accessioned2026-04-30T09:44:41Z
dc.date.available2026-04-30T09:44:41Z
dc.date.issued2026
dc.description.abstract-translatedThis paper presents the development and experimental evaluation of an intelligent system using long-wave infrared (LWIR) polarimetric imaging combined with machine learning. By exploiting polarization-based contrast mechanisms, the approach improves detection of surface features that are difficult to identify with conventional methods. The work includes the design of an experimental setup and the creation of a representative LWIR polarimetric dataset. A modular framework integrating convolutional neural networks and image quality metrics enables automated scene interpretation. Results demonstrate enhanced detection of transport-related phenomena such as thin liquid films, surfaces, and hidden contamination, supporting future mobility and safety applications.en
dc.description.sponsorshipTQ15000307en
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.isbn978-80-261-1352-2
dc.identifier.urihttp://hdl.handle.net/11025/67904
dc.language.isoenen
dc.publisherUniversity of West Bohemia in Pilsenen
dc.rights© University of West Bohemia in Pilsenen
dc.rights.accessopenAccessen
dc.subjectLWIR polarimetriecs
dc.subjectstrojové učenícs
dc.subjectmonitorování transportucs
dc.subjectdetekce anomáliícs
dc.subject.translatedLWIR polarimetryen
dc.subject.translatedmachine learningen
dc.subject.translatedtransport monitoringen
dc.subject.translatedanomaly detectionen
dc.titleDevelopment and experimental evaluation of a machine learning assisted lwir polarimetric imaging system for transport anomaly detectionen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.versionpublishedVersionen
local.files.count2*
local.files.size1440721*
local.has.filesyes*

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