Canadian Forest Service Publications

Knot detection in computed tomography images of partially dried Jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation. 2017. Fredriksson, M.; Cool, J.; Duchesne, I.; Belley, D. Can. J. For. Res. 47:910-915.

Year: 2017

Issued by: Laurentian Forestry Centre

Catalog ID: 38205

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1139/cjfr-2016-0423

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Abstract

X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored, they dry to some extent, and this drying affects the density variation in the log and therefore the X-ray images. For this reason, it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. This paper investigates the effect of improper heartwood–sapwood border detection, possibly due to partial drying, on knot detection in jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual reference knot measurements, and the results showed that knot detection was affected by detected heartwood shape. It was also shown that logs can be sorted into two groups based on how well the heartwood–sapwood border is detected to separate logs with a high knot detection rate from those with a low detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. This can potentially aid both sawmills and researchers working with log models based on CT.

Plain Language Summary

This study showed that partially drying jack pine and white spruce logs had an impact on the knot detection rate in computerized tomography scans. Computerized tomography is an X-ray imaging technique used in processing plants to identify certain internal characteristics of logs based on wood density variations.

Results also show that it would be appropriate to sort logs beforehand using computerized tomography to divide them into two groups based on the presence or absence of a sapwood-heartwood transition line. The knot detection model to be used depends in part on the precise detection of this line. Sawing logs with a high knot detection rate would therefore be optimized according to internal characteristics, whereas logs with a lower knot detection rate should be sawed solely based on their external shape.

Logs dry unevenly when stored, which causes wood density variations and complicates the detection of knots using computerized tomography. The results of this study will improve the in-plant sawing process through a better evaluation of the logs’ internal quality.