Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Tunneling and Rock Mechanics · Industrial Vision Systems and Defect Detection · Wood Treatment and Properties
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Affiliations
The authors are, respectively, Associate Senior Lecturer, Luleå Univ. of Technol., Skellefteå, Sweden (magnus.1.fredriksson@ltu.se); and Assistant Professor and Professor, Dept. of Wood Sci., Faculty of Forestry, The Univ. of British Columbia, Vancouver, British Columbia, Canada (julie.cool@ubc.ca [corresponding author], stavros.avramidis@ubc.ca). Stavros Avramidis is a member of the Forest Products Society. This paper was received for publication in February 2019. Article no. 19-00008.
References (9)
Show source-provided references
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