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Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.ORCID iD: 0000-0003-4530-0536
Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada.
Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada. Forest Prod Soc, Peachtree Corners, GA USA.
2019 (English)In: Forest products journal, ISSN 0015-7473, Vol. 69, no 3, p. 185-187Article in journal (Refereed) Published
Abstract [en]

X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.

Place, publisher, year, edition, pages
Forest Products Society, 2019. Vol. 69, no 3, p. 185-187
National Category
Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-76189DOI: 10.13073/FPJ-D-19-00008ISI: 000484574100002OAI: oai:DiVA.org:ltu-76189DiVA, id: diva2:1356481
Note

Validerad;2019;Nivå 2;2019-10-01 (johcin)

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-12-10Bibliographically approved

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Fredriksson, Magnus

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