A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory

• URL: http://arxiv.org/abs/2505.01656v1
• Date: Sat, 03 May 2025 02:19:55 GMT
• Title: A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory
• Authors: Chenyang Fan, Xujie Zhu, Taige Luo, Sheng Xu, Zhulin Chen, Hongxin Yang,
• Abstract summary: This work proposes a novel WaveInst instance segmentation framework, involving a discrete wavelet transform, to improve tree structure extraction.<n> Experimental results of the proposed model show superior performance on SynthTree43k, CaneTree100, Urban Street and our PoplarDataset.<n>The proposed method achieves a mean average precision of 49.6 and 24.3 for the structure extraction of mature and juvenile trees, respectively, surpassing the existing state-of-the-art method by 9.9.
• Score: 2.793797265684592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The pattern analysis of tree structure holds significant scientific value for genetic breeding and forestry management. The current trunk and branch extraction technologies are mainly LiDAR-based or UAV-based. The former approaches obtain high-precision 3D data, but its equipment cost is high and the three-dimensional (3D) data processing is complex. The latter approaches efficiently capture canopy information, but they miss the 3-D structure of trees. In order to deal with the branch information extraction from the complex background interference and occlusion, this work proposes a novel WaveInst instance segmentation framework, involving a discrete wavelet transform, to enhance multi-scale edge information for accurately improving tree structure extraction. Experimental results of the proposed model show superior performance on SynthTree43k, CaneTree100, Urban Street and our PoplarDataset. Moreover, we present a new Phenotypic dataset PoplarDataset, which is dedicated to extract tree structure and pattern analysis from artificial forest. The proposed method achieves a mean average precision of 49.6 and 24.3 for the structure extraction of mature and juvenile trees, respectively, surpassing the existing state-of-the-art method by 9.9. Furthermore, by in tegrating the segmentation model within the regression model, we accurately achieve significant tree grown parameters, such as the location of trees, the diameter-at-breast-height of individual trees, and the plant height, from 2D images directly. This study provides a scientific and plenty of data for tree structure analysis in related to the phenotype research, offering a platform for the significant applications in precision forestry, ecological monitoring, and intelligent breeding.

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