Automatic detection of pith location along norway spruce timber boards on the basis of optical scanning

Abstract

Knowledge of annual ring width and location of pith in relation to board cross-sections, and how these properties vary in the longitudinal direction of boards, is relevant for many purposes, such as assessment of shape mechanical properties and stability of sawn timber. Hence, the present research aims at developing a novel method and an algorithm, based on data obtained from optical surface scanning, by which the pith location along the length of sawn timber boards can be determined accurately and automatically. The first step of the method is to identify clear wood sections, free of defects along boards. Then time-frequency analysis, using the continuous wavelet transform, is applied to detect the surface annual ring width distribution of the four sides of the selected sections. Finally, the pith location is estimated by comparing annual ring width distributions on the different surfaces, and assuming that annual rings are concentric circles with the pith in the centre. The proposed algorithm was applied to a total sample of 104 Norway spruce boards. Results indicate that optical scanners and the suggested automatic method allow for accurate detection of annual ring width and location of pith along boards. For a sample of boards with the pith located within the cross-section, a mean error of 2.6 mm and 3.2 mm in the depth and thickness direction, respectively, was obtained. For a sample of boards of which 60% with pith located outside the cross-section, a mean discrepancy between automatically and manually determined pith locations of 3.9 mm and 5.8 mm in depth and thickness direction, respectively, was obtained.

Constructing Virtual Representations of Laminated Timber Products

“…There also appears to be limited literature regarding automated algorithms for locating the pith when only given an image of the end grain of a timber board. Habite, et al (Habite et al, 2020(Habite et al, , 2022 used two neural networks to locate the pith of Norway spruce (Picea abies) timber boards to great success. The first used a conditional generative adversarial network (cGAN) (Habite et al, 2020), and the latter used a one-dimensional convolutional neural network (Habite et al, 2022).…”

“…Habite, et al (Habite et al, 2020(Habite et al, , 2022 used two neural networks to locate the pith of Norway spruce (Picea abies) timber boards to great success. The first used a conditional generative adversarial network (cGAN) (Habite et al, 2020), and the latter used a one-dimensional convolutional neural network (Habite et al, 2022). Moreover, it is difficult to estimate the location of the pith using the plastic sheet of concentric circles described by Habite, et al's (Habite et al, 2022(Habite et al, , 2020.…”

“…The first used a conditional generative adversarial network (cGAN) (Habite et al, 2020), and the latter used a one-dimensional convolutional neural network (Habite et al, 2022). Moreover, it is difficult to estimate the location of the pith using the plastic sheet of concentric circles described by Habite, et al's (Habite et al, 2022(Habite et al, , 2020. Some other work by Briggert, et al (Briggert et al, 2016) used tracheid-effect scanning to determine the fibre orientation to model the shape of knots and to locate the pith for Norway spruce.…”

“…This allows the material properties that are computed in the radial, transverse and longitudinal directions to be represented using the x, y and z directions. Recent publications have used a similar approach to locate the pith of Norway spruce timber (Habite et al, 2020), to achieve good estimations of the pith locations and average annual growth rate (growth ring width). They assumed that the growth ring width is constant throughout the whole board, which for Norway spruce was a valid assumption.…”

Performance of timber board models for prediction of local bending stiffness and strength - with application on Douglas fir sawn timber

“…Regarding location of pith, new methods to determine this have been developed in the last few years. Habite et al (2020) presented a method to automatically determine location of pith in relation to board cross sections, based on optical scanning of the four longitudinal surfaces of boards and identification of annular ring pattern on images of the surfaces. Faster and more robust methods, based on optical scanning of longitudinal board surfaces in combination with machine learning, were developed by Habite et al (2021) and in particular Habite et al (2022).…”