A fully automated technique for wood pith detection (APD), relying on the
concentric shape of the structure of wood ring slices, is introduced. The
method estimates the ring's local orientations using the 2D structure tensor
and finds the pith position, optimizing a cost function designed for this
problem. We also present a variant (APD-PCL), using the parallel coordinates
space, that enhances the method's effectiveness when there are no clear tree
ring patterns. Furthermore, refining previous work by Kurdthongmee, a YoloV8
net is trained for pith detection, producing a deep learning-based approach to
the same problem (APD-DL). All methods were tested on seven datasets, including
images captured under diverse conditions (controlled laboratory settings,
sawmill, and forest) and featuring various tree species (Pinus taeda, Douglas
fir, Abies alba, and Gleditsia triacanthos). All proposed approaches outperform
existing state-of-the-art methods and can be used in CPU-based real-time
applications. Additionally, we provide a novel dataset comprising images of
gymnosperm and angiosperm species. Dataset and source code are available at
this http URL

一种基于木材环切片结构的完全自动化的木材髓心检测技术（APD）被引入。该方法利用 2D 结构张量估计环的局部方向，通过优化设计用于此问题的成本函数来找到髓心位置。我们还提出了一种变体（APD-PCL），利用平行坐标空间，以增强该方法在没有明确树环图案的情况下的有效性。此外，通过对基于深度学习的方法（APD-DL）进行 Kurdthongmee 以前工作的改进，训练了一个用于髓心检测的 YoloV8 神经网络，解决了同一问题。所有方法都在包括在不同条件下获取的图像（控制实验室环境、锯木厂和森林）以及包含各种树种（美国油松、道格拉斯树木、白冷杉和皂荚）的七个数据集上进行了测试。所有提出的方法都优于现有的最先进方法，并可用于基于 CPU 的实时应用。此外，我们提供了一个包含了裸子植物和被子植物物种图像的新颖数据集。数据集和源代码可以在此 http 网址上获取。