Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorVihlman, Mikko
dc.contributor.authorHyyti, Heikki
dc.contributor.authorKalmari, Jouko
dc.contributor.authorVisala, Arto
dc.contributor.departmentSähkötekniikan ja automaation laitosfi
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.labAutonomous systemsen
dc.contributor.labAutonomiset järjestelmätfi
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.schoolSchool of Electrical Engineeringen
dc.date.accessioned2016-01-18T10:01:40Z
dc.date.available2016-01-18T10:01:40Z
dc.date.issued2015
dc.description.abstractAn approach to automatically detect and classify young spruce and birch trees in forest environment is presented. The method could be used in autonomous or semi-autonomous forest machines during tending operations. Detection is done by segmenting laser range images formed by a rotating laser scanner. Classification is done with a two-class Naive Bayes classifier based on image texture features. Multiple combinations of 99 features were tested and the best classifier included eight features from the co-occurrence matrix, local binary patterns, statistical geometrical features and Gabor filter. 79% of spruces and birches in the testing material were detected and 74% of these were correctly classified. Results suggest that the approach is suitable but there are still some challenges in each of the processing steps. Iteration between segmentation and classification is needed to increase reliability.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen
dc.identifier.citationVihlman, Mikko & Hyyti, Heikki & Kalmari, Jouko & Visala, Arto. 2015. Detection and species classification of young trees using machine perception for a semi-autonomous forest machine. IEEE International Conference on Robotics and Automation (ICRA). 6 p. DOI: 10.1109/icra.2015.7139394.en
dc.identifier.doi10.1109/icra.2015.7139394
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/19367
dc.identifier.urnURN:NBN:fi:aalto-201601151015
dc.language.isoenen
dc.publisherInstitute of Electrical & Electronics Engineers (IEEE)en
dc.relation.ispartofIEEE International Conference on Robotics and Automation (ICRA)en
dc.rights© 2015 Institute of Electrical & Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.en
dc.rights.holderInstitute of Electrical & Electronics Engineers (IEEE)
dc.subject.keywordmachine visionen
dc.subject.keywordsegmentationen
dc.subject.keywordcategorizationen
dc.subject.keywordroboticsen
dc.subject.otherAutomationen
dc.titleDetection and species classification of young trees using machine perception for a semi-autonomous forest machineen
dc.typeA4 Artikkeli konferenssijulkaisussafi
dc.type.dcmitypetexten
dc.type.versionPost printen
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