| 基于SegNet与三维点云聚类的大田杨树苗叶片分割方法 |
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| 引用本文： | 胡春华,刘炫,计铭杰,李羽江,李萍萍.基于SegNet与三维点云聚类的大田杨树苗叶片分割方法[J].农业机械学报,2022,53(6):259-264. |
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| 作者姓名： | 胡春华 刘炫 计铭杰 李羽江 李萍萍 |
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| 作者单位： | 1. 南京林业大学信息科学技术学院;2. 南京林业大学生物与环境学院 |
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| 基金项目： | 国家重点研发计划项目(2017YFD0600905-1)； |
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| 摘 要： | 准确分割单个杨树叶是无接触提取杨树苗叶表型参数的前提，针对大田杨树苗的复杂种植环境，本文提出一种基于SegNet与三维点云聚类的大田杨树苗叶片分割方法。首先对Kinect V2相机进行标定，对齐RGB与深度数据，滤除背景，获得RGB与深度数据融合数据；然后针对RGB与深度融合数据采用语义分割算法SegNet对杨树苗叶与杨树干进行分割；为了更好地分割出单个杨树叶，对分割的杨树叶区域重构出三维点云，采用基于几何距离的kd-tree对单个树叶进行分类。对采集的单株树苗与多株树苗数据进行了实验分析，采用SegNet与FCN分别对杨树苗叶区域与茎区域进行分割，结果表明，SegNet对叶、茎检测准确率分别为94.4%、97.5%,交并比分别为75.9%、67.9%,优于FCN;对叶区域采用不同距离阈值的kd-tree算法进行单叶分割分析，确定了适合杨树叶的分割阈值。实验结果表明，本文提出的分割算法不仅能分割出单株杨树苗的叶片，也能分割出多株杨树苗的单个叶片。
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| 关 键 词： | 杨树叶片 SegNet 三维点云 分割 |
| 收稿时间： | 2021/6/29 0:00:00 |
Single Poplar Leaf Segmentation Method Based on SegNet and 3D Point Cloud Clustering in Field |
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| HU Chunhu,LIU Xuan,JI Mingjie,LI Yujiang,LI Pingping.Single Poplar Leaf Segmentation Method Based on SegNet and 3D Point Cloud Clustering in Field[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(6):259-264. |
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| Authors: | HU Chunhu LIU Xuan JI Mingjie LI Yujiang LI Pingping |
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| Institution: | Nanjing Forestry University |
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| Abstract: | Automatic and accurate segmenting a single poplar leaf is very necessary for non-contact extraction of plant leaf phenotype. However, a single leaf segmentation is a challenging task, especially for the complexity of field poplar seedling planting environment. An automatic leaf segmentation method combined SegNet with 3D point cloud clustering was proposed. In the proposed approach, to obtain accurate sample images, the Kinect V2 camera was firstly calibrated. Subsequently, the RGB and depth data were aligned, the background was filtered, and the RGB and deep fusion data of poplar seedling were collected. Then, for RGB and deep fusion data, a large number of samples were labelled and SegNet was utilized to segment poplar seedling leaf and trunk candidate regions. Finally, in order to better segment single poplar leaves, 3D point cloud of leaf regions were reconstructed by using the RGB-D fusion data of poplar leaf regions separated by SegNet, and kd-tree based on geometric distance was introduced to classify single leaves. The performance of the proposed method was verified by various comparative experiments for poplar seedlings in different growth environments. SegNet and FCN were used to segment the leaf region and stem region of poplar seedlings respectively. The results showed that the precision of SegNet for leaf and stem detection were 94.4% and 97.5% respectively, and the intersection over union (IoU) were 75.9% and 67.9% respectively, which was better than that of FCN. In order to find the suitable segmentation threshold for a single poplar leaf segmentation, the comparison experiments of different threshold segmentation using kd-tree for single and multiple poplar seedling leaf areas were performed. The experiment results validated that the proposed method can segment poplar leaves not only for a single poplar seedling, but also for multiple poplar seedlings. |
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