This work presents the INBD network proposed by Gillert et al. in CVPR-2023 and studies its application for delineating tree rings in RGB images of Pinus taeda cross sections captured by a smartphone (UruDendro dataset), which are images with different characteristics from the ones used to train the method. The INBD network operates in two stages: first, it segments the background, pith, and ring boundaries. In the second stage, the image is transformed into polar coordinates, and ring boundaries are iteratively segmented from the pith to the bark. Both stages are based on the U-Net architecture. The method achieves an F-Score of 77.5, a mAR of 0.540, and an ARAND of 0.205 on the evaluation set. The code for the experiments is available at https://github.com/hmarichal93/mlbrief_inbd.

翻译：本研究介绍了Gillert等人在CVPR-2023提出的INBD网络，并探讨其在智能手机拍摄的火炬松横截面RGB图像（UruDendro数据集）中树轮轮廓提取的应用。这些图像与训练该方法所用数据具有不同的特征。INBD网络分两阶段运行：首先对背景、髓心和年轮边界进行分割；第二阶段将图像转换为极坐标系，并从髓心到树皮迭代分割年轮边界。两个阶段均基于U-Net架构。该方法在评估集上取得了77.5的F分数、0.540的mAR和0.205的ARAND指标。实验代码发布于https://github.com/hmarichal93/mlbrief_inbd。