ODNet: Orthogonal-Perception and Dense-dilation Enhanced Network for Segmenting Complex Tree Branch Structures

Xin Zhou, Tianyang Dong, Jing Fan, Wenyuan Ying, Hubin Kong

Abstract: Accurate delineation and characterization of tree branch structures are vital for assessing the status of trees, forecasting the risks of diseases, and formulating conservation strategies. Current segmentation methods struggle to effectively handle the intricacies and diversity present in branch structures, particularly branches that are slender or obscured by dense leaves. To address these issues, this paper introduces the Occlusion-perception and Dense-dilation Network (ODNet), an innovative network designed to enhance tree branch segmentation and reduce occlusion impacts. Firstly, the Dense Dilated Module within ODNet enhances adaptability to diverse tree scales, addressing species variations and complex branch structures. Secondly, drawing insights from tree growth patterns that usually grow upwards and outwards, we designed Orthogonal Perception Module (OPM) . OPM offers a comprehensive perspective of tree branch structures, which can mitigate the prediction discontinuities caused by leaves occlusion. The quantitative analysis and visual comparisons indicate that ODNet achieves better results than existing methods.