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

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Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering | A4 Artikkeli konferenssijulkaisussa
Date
2013
Major/Subject
Mcode
Degree programme
Language
en
Pages
2984-2991
Series
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.
Description
Keywords
machine vision, color and texture features, spruce detection, real-time computing, CUDA GPU
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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.