A Brief Analysis of the Iterative Next Boundary Detection Network for Tree Rings Delineation in Images of Pinus taeda

Henry Marichal, Gregory Randall
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Abstract

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.
简要分析迭代下一边界检测网络在太行松图像中的树环划分
本研究介绍了 Gillert 等人在 CVPR-2023 中提出的 INBD 网络,并研究了该网络在智能手机捕捉的 Pinustaeda 横截面 RGB 图像(UruDendro 数据集)中划分树环的应用,这些图像与用于训练该方法的图像具有不同的特征。在第二阶段,将图像转换为极坐标,从髓部到树皮对环状边界进行迭代分割。这两个阶段都基于 U-Net 架构。该方法在评估集上的 F 分数为 77.5,mAR 为 0.540,ARAND 为 0.205。实验代码可在https://github.com/hmarichal93/mlbrief_inbd。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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