Taige Luo | Searcharxiv

arXiv2026

LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark

Intelligent forest tree breeding has advanced plant phenotyping, yet existing research largely focuses on large-leaf agricultural crops, with limited attention to fine-grained leaf analysis of sapling trees in open-field environments. Natural scenes introduce challenges including scale variation, illumination changes, and irregular leaf morphology. To address these issues, we collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances. To our knowledge, this is the first instance segmentation dataset specifically designed for forestry leaves in open-field conditions. We propose LeafInst, a novel segmentation framework tailored for irregular and multi-scale leaf structures. The model integrates an Asymptotic Feature Pyramid Network (AFPN) for multi-scale perception, a Dynamic Asymmetric Spatial Perception (DASP) module for irregular shape modeling, and a dual-residual Dynamic Anomalous Regression Head (DARH) with Top-down Concatenation decoder Feature Fusion (TCFU) to improve detection and segmentation performance. On Poplar-leaf, LeafInst achieves 68.4 mAP, outperforming YOLOv11 by 7.1 percent and MaskDINO by 6.5 percent. On the public PhenoBench benchmark, it reaches 52.7 box mAP, exceeding MaskDINO by 3.4 percent. Additional experiments demonstrate strong generalization and practical utility for large-scale leaf phenotyping.

cs.CV↗

arXiv2025

WaveInst: A Frequency-Domain Enhanced Network for Fine-Grained Thin Tree Trunk Extraction in Forest Scenes

Analyzing tree morphology, particularly trunk and branch extraction, is valuable for genetic breeding and forestry management. Existing image-based deep learning methods tend to misidentify overlapping trunks as a single trunk when structural discontinuities occur due to front-back overlap, while low contrast between trunk textures and the background further complicates segmentation. Moreover, limited juvenile tree data, coupled with substantial variations in trunk diameter across growth stages, restricts model performance in extracting thin trunks and branches. Based on this, we propose an instance segmentation network leveraging frequency-domain features, WaveInst. Its core is a Frequency-domain Feature Compensation branch, consisting of Discrete Wavelet Transform block and High-Frequency Enhancement block. The former performs high- and low-frequency decomposition and aggregates high-frequency responses along multiple directions, while the latter further refines the high-frequency features through multi-path processing. An Adaptive Gated Fusion Module is then applied to effectively integrate spatial-domain convolutional features with frequency-domain representations, allowing the decoder to utilize embedded frequency-domain features to enhance its fine-grained detail representation. We conduct experiments on public datasets including SynthTree43k, CaneTree100, and UrbanStreet, as well as PoplarDataset, which contains both mature and juvenile poplar trees. On the public datasets, WaveInst demonstrates strong performance and stable robustness across diverse scenarios. On PoplarDataset, it achieves a mean average precision of 53.1 for mature and 29.1 for juvenile, outperforming existing state-of-the-art methods on juvenile by 6.6 points, demonstrating its effectiveness in extracting thin tree trunk.

cs.CV↗