论文标题
基于图的方法用于使用嘈杂点云数据分析果园树结构
Graph-based methods for analyzing orchard tree structure using noisy point cloud data
论文作者
论文摘要
使用LiDar对果树进行数字化实现分析，可用于改善增长的实践以提高产量。复杂的分析需要对数据的几何和语义理解，包括辨别单个树木的能力以及识别绿叶和结构物质。这些信息的提取应该是迅速的，数据捕获也应该很快，以便可以处理整个果园，但是现有的分类和细分方法依赖于高质量的数据或其他数据源（例如相机）。我们提出了一种专门针对单个树位置，分割和物质分类的LIDAR数据的方法，该数据可以在手持式或移动激光雷达捕获的低质量数据上操作。我们针对树位置和分割的方法在现有方法上有所改进，F1得分为0.774和V量表分别为0.915，而Trunk Matter分类在绝对方面的表现较差，而实际数据的平均F1得分为0.490，尽管实际上却持续了现有方法，并且表现出明显较短的运行时间。
Digitisation of fruit trees using LiDAR enables analysis which can be used to better growing practices to improve yield. Sophisticated analysis requires geometric and semantic understanding of the data, including the ability to discern individual trees as well as identifying leafy and structural matter. Extraction of this information should be rapid, as should data capture, so that entire orchards can be processed, but existing methods for classification and segmentation rely on high-quality data or additional data sources like cameras. We present a method for analysis of LiDAR data specifically for individual tree location, segmentation and matter classification, which can operate on low-quality data captured by handheld or mobile LiDAR. Our methods for tree location and segmentation improved on existing methods with an F1 score of 0.774 and a v-measure of 0.915 respectively, while trunk matter classification performed poorly in absolute terms with an average F1 score of 0.490 on real data, though consistently outperformed existing methods and displayed a significantly shorter runtime.
