Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation

Abstract

1X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in 2 sawmills. This depends on accurate detection of knots to assess internal quality. However, as 3 logs are stored they dry to some extent, and this drying affects the density variation in the log, 4 and therefore the X-ray images. For this reason it is hypothetically difficult to detect log 5 features in partially dried logs using X-ray CT. This paper investigates the effect of improper 6 heartwood-sapwood border detection, possibly due to partial drying, on knot detection in jack 7 pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New 8 Brunswick, Canada. An automatic knot detection algorithm was compared to manual 9 reference knot measurements, and the results showed that knot detection was affected by 10 detected heartwood shape. It was also shown that logs can be sorted into two groups based on 11 how well the heartwood-sapwood border is detected, to separate logs with a high knot 12 detection rate from those with a low detection rate. In that way, a decision can be made 13 whether or not to trust the knot models obtained from CT scanning. This can potentially aid 14 both sawmills and researchers working with log models based on CT.

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

“…They obtained detection rates (false positives) of 87.3% (1.9%) and 71.2% (4.9%) for regular heartwood groups of jack pine and white spruce logs, respectively. The knot detection rate corroborates the robustness behind the construction of the algorithm (Johansson et al 2013) and its applicability potential to other softwood species (Fredriksson et al 2017), especially when the logs are fresh.…”

“…7a1. A similar pattern was also observed by Johansson et al (2013) for Norway spruce and can be attributed to the low contrast between the knot and the surrounding wood within the saturated sapwood, a problem frequently raised in the literature (Breinig et al 2012;Fredriksson et al 2017;Funt and Bryant 1987;Johansson Longuetaud et al 2012;Wei et al 2009). Despite different approaches being developed to overcome this issue (Johansson et al 2013;Krähenbühl et al 2016;Roussel et al 2014), it remains not completely solved.…”

“…Using manual measurements on CT images as a reference method to validate their algorithms, Johansson et al (2013) found total diameter deviation values of 4.6 mm for Scots pine and 5.1 mm for Norway spruce. Applying the same methods, Fredriksson et al (2017) observed an accuracy in total diameter of 4.9 mm for regular and 6.2 mm for irregular heartwood groups of jack pine and white spruce logs. The study used an image resolution of 0.605 × 0.605 × 1 mm in contrast to 1.107 × 1.107 × 5 mm applied in the present study, which may explain the observed difference in performance, aside from the different analyzed species.…”

“…Given the log length scale (between 4 and 5 m), the accuracy in longitudinal knot position was satisfactory. Johansson et al (2013) observed an accuracy of 7.8 (Norway spruce) and 9.2 mm (Scots pine) for this variable, while Fredriksson et al (2017) reported 7.03 mm for a mix of jack pine and white spruce logs with regular heartwood boundary. The probable Fig.…”

“…Nonetheless, when comparing the number of false positives, the present study reports 1.5%, while the previously mentioned work reports about 1% of overdetection. Fredriksson et al (2017) split a mixed dataset of partially dried jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss.)…”