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

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School of Electrical Engineering | A4 Artikkeli konferenssijulkaisussa
Date
2015
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Mcode
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Language
en
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Abstract
An 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.
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Keywords
machine vision, segmentation, categorization, robotics
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Citation
Vihlman, 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.