Detection Of Road Surface Defects From Data Acquired By A Laser Scanner

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but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorMyska, Vojtech
dc.date.accessioned2023-01-06T10:05:42Z
dc.date.available2023-01-06T10:05:42Z
dc.date.issued2021cs
dc.description.abstractResearch in the field of automatic detection of road surface defects has been relativelywidespread in recent years. Most of the existing works solve this issue by processing the imageacquired by camera technology. The contribution of this study is the proposal of the LRS-CNN algorithmfor the detection of defects on road surfaces based on their laser scans. The advantage ofLRS-CNN is the ability to detect so-called microcracks, which can not be recognized from camerarecordings. We have also found that transfer learning methods are not suitable for the use of road defectdetection from their laser scans. Our LRS-CNN algorithm has been trained on unique nonpublicdata and is able to achieve up to 99.33% of success depending on the type of task.en
dc.formattextcs
dc.format.extent275-279cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 275-279. ISBN 978-80-214-5943-4cs
dc.identifier.doi10.13164/eeict.2021.275
dc.identifier.isbn978-80-214-5943-4
dc.identifier.urihttp://hdl.handle.net/11012/200857
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 27st Conference STUDENT EEICT 2021: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectroad damage detectionen
dc.subjectroad surface laser scanen
dc.subjectdeep learningen
dc.titleDetection Of Road Surface Defects From Data Acquired By A Laser Scanneren
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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