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Real-time Detection of Young Spruce Using Color and Texture Features on an Autonomous Forest Machine

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hyyti, Heikki
dc.contributor.author Kalmari, Jouko
dc.contributor.author Visala, Arto
dc.date.accessioned 2015-01-21T10:00:21Z
dc.date.available 2015-01-21T10:00:21Z
dc.date.issued 2013
dc.identifier.citation Hyyti, Heikki & Kalmari, Jouko & Visala, Arto. 2013. Real-time Detection of Young Spruce Using Color and Texture Features on an Autonomous Forest Machine. The 2013 International Joint Conference on Neural Networks. P. 2984-2991. ISSN 2161-4393 (printed). ISBN 978-1-4673-6128-6 (printed). DOI: 10.1109/IJCNN.2013.6707122. en
dc.identifier.isbn 978-1-4673-6128-6 (printed)
dc.identifier.issn 2161-4393 (printed)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/14991
dc.description.abstract Forest machines are manually operated machines that are efficient when operated by a professional. Point cleaning is a silvicultural task in which weeds are removed around a young spruce tree. To automate point cleaning, machine vision methods are used for identifying spruce trees. A texture analysis method based on the Radon and wavelet transforms is implemented for the task. Real-time GPU implementation of algorithms is programmed using CUDA framework. Compared to a single thread CPU implementation, our GPU implementation is between 18 to 80 times faster depending on the size of image blocks used. Color information is used in addition of texture and a location estimate of the tree is extracted from the detection result. The developed spruce detection system is used as a part of an autonomous point cleaning machine. To control the system, an integrated user interface is presented. It allows the operator to control, monitor and train the system online. en
dc.format.extent 2984-2991
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartof The 2013 International Joint Conference on Neural Networks en
dc.rights 2013 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 works. en
dc.subject.other Automation en
dc.subject.other Computer science en
dc.title Real-time Detection of Young Spruce Using Color and Texture Features on an Autonomous Forest Machine en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.rights.holder IEEE
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.contributor.school School of Electrical Engineering en
dc.contributor.department Department of Automation and Systems Technology en
dc.contributor.department Automaatio- ja systeemitekniikan laitos fi
dc.subject.keyword machine vision en
dc.subject.keyword color and texture features en
dc.subject.keyword spruce detection en
dc.subject.keyword real-time computing en
dc.subject.keyword CUDA GPU en
dc.identifier.urn URN:NBN:fi:aalto-201501221181
dc.type.dcmitype text en
dc.identifier.doi 10.1109/IJCNN.2013.6707122
dc.contributor.lab Autonomous systems en
dc.contributor.lab Autonomiset järjestelmät fi
dc.type.version Post print en

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