Improving object detection in adverse weather conditions for Auve Tech’s autonomous vehicle

dc.contributor.authorAbbasov, Elmar
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.date.accessioned2022-07-01T09:53:53Z
dc.date.available2022-07-01T09:53:53Z
dc.date.issued2022
dc.description.abstractAs autonomous vehicles are becoming more prominent in our lives we want their computing systems to be able to recognize objects with the best accuracy possible, regardless of the weather conditions. In order to achieve better accuracy with machine learning based visual object detection we compare 2 approaches: training an object detection neural network with synthetic rain added images and removing rain from images using a different state-of-the-art neural network before feeding them to an object detection neural network trained with non-rainy images.et
dc.identifier.urihttp://hdl.handle.net/10062/83019
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectObject detectionet
dc.subjectYOLOet
dc.subjectderaininget
dc.subjectautonomous drivinget
dc.subjectadverse weather conditionset
dc.subjectObjekti tuvastuset
dc.subjectvihma-eemalduset
dc.subjectautonoomne liikumineet
dc.subjectebasoodsad ilmastikuoludet
dc.subject.othermagistritöödet
dc.titleImproving object detection in adverse weather conditions for Auve Tech’s autonomous vehicleet
dc.typeThesiset

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