Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs

Document identifier: oai:DiVA.org:ltu-76189
Access full text here:10.13073/FPJ-D-19-00008
Keyword: Engineering and Technology, Mechanical Engineering, Other Mechanical Engineering, Teknik och teknologier, Maskinteknik, Annan maskinteknik, Träteknik, Wood Science and Engineering
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Magnus Fredriksson

Luleå tekniska universitet; Träteknik
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Julie Cool

Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada
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Avramidis Stavros

Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada. Forest Prod Soc, Peachtree Corners, GA USA
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