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

TL;DR

This paper evaluates the INBD network for delineating tree rings in smartphone-captured images of Pinus taeda, demonstrating its effectiveness despite differences from training data, with promising segmentation metrics.

Contribution

It applies and assesses the INBD network for tree ring segmentation in real-world smartphone images, highlighting its adaptability and performance in practical scenarios.

Findings

F-Score of 77.5 achieved

Effective segmentation of tree rings in diverse images

Code and methodology are publicly available

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.