In the woodworking industry, detection of annual rings and location of pith in relation to timber board cross sections, and how these properties vary in the longitudinal direction of boards, is relevant for many purposes such as assessment of shape stability and prediction of mechanical properties of timber. The current work aims at developing a fast, accurate and operationally simple deep learning-based algorithm for automatic detection of surface growth rings and pith location along knot-free clear wood sections of Norway spruce boards. First, individual surface growth rings that are visible along the four longitudinal sides of the scanned boards are detected using trained conditional generative adversarial networks (cGANs). Then, pith locations are determined, on the basis of the detected growth rings, by using a trained multilayer perceptron (MLP) artificial neural network. The proposed algorithm was solely based on raw images of board surfaces obtained from optical scanning and applied to a total of 104 Norway spruce boards with nominal dimensions of \(45\times 145\times 4500\,\hbox {mm}^{3}\). The results show that optical scanners and the proposed automatic method allow for accurate and fast detection of individual surface growth rings and pith location along boards. For boards with the pith located within the cross section, median errors of 1.4 mm and 2.9 mm, in the x- and y-direction, respectively, were obtained. For a sample of boards with the pith located outside the board cross section in most positions along the board, the median discrepancy between automatically estimated and manually determined pith locations was 3.9 mm and 5.4 mm in the x- and y-direction, respectively.

在木工行业中，检测年轮和相对于木板横截面的髓的位置，以及这些属性在木板的纵向方向上如何变化，与许多目的相关，例如评估形状稳定性和预测木材的机械性能。木材。当前的工作旨在开发一种基于深度学习的快速，准确和操作简单的算法，用于自动检测挪威云杉板的无结透明木节表面的年轮和髓的位置。首先，使用经过训练的条件生成对抗网络（cGAN）检测沿扫描板的四个纵向侧面可见的各个表面生长环。然后，根据检测到的年轮确定髓位，通过使用训练有素的多层感知器（MLP）人工神经网络。所提出的算法仅基于通过光学扫描获得的板材表面的原始图像，并应用于总共104个标称尺寸为的挪威云杉板材。\（45 \ times 145 \ times 4500 \，\ hbox {mm} ^ {3} \）。结果表明，光学扫描仪和提出的自动方法可以准确，快速地检测单个表面生长环和沿木板的髓位置。对于髓位于横截面内的板，在x和y方向上的中值误差分别为1.4 mm和2.9 mm。对于沿木板在大多数位置上的髓位于木板横截面之外的木板样品，自动估算和手动确定的髓位置之间的中值差异在x方向和y方向分别为3.9 mm和5.4 mm。