Abstract
Here, we propose Deep CS-TRD, a new automatic algorithm for detecting tree rings in whole cross-sections. It substitutes the edge detection step of CS-TRD by a deep-learning-based approach (U-Net), which allows the application of the method to different image domains: microscopy, scanner or smartphone acquired, and species (Pinus taeda, Gleditsia triachantos and Salix glauca). Additionally, we introduce two publicly available datasets of annotated images to the community. The proposed method outperforms state-of-the-art approaches in macro images (Pinus taeda and Gleditsia triacanthos) while showing slightly lower performance in microscopy images of Salix glauca. To our knowledge, this is the first paper that studies automatic tree ring detection for such different species and acquisition conditions. The dataset and source code are available in this https URL
Abstract (translated)
在这里，我们提出了Deep CS-TRD，这是一种新的用于在整木横截面上自动检测年轮的算法。该算法用基于深度学习的方法（U-Net）替代了CS-TRD中的边缘检测步骤，这使得该方法可以应用于不同的图像领域：包括显微镜、扫描仪或智能手机获取的图像以及不同种类的树木（如Pinus taeda, Gleditsia triacanthos 和 Salix glauca）。此外，我们还向社区引入了两个公开可用的带有标注的图像数据集。所提出的方法在宏观图像（Pinus taeda和Gleditsia triacanthos）上超越了现有技术方法的表现，并且在Salix glauca的显微镜图像上的表现略低一些。据我们所知，这是第一篇研究针对不同种类树木及获取条件下的自动年轮检测的论文。数据集和源代码可在该网址获得：[此链接]
URL
https://arxiv.org/abs/2504.16242
