• Chinese Journal of Lasers
  • Vol. 51, Issue 8, 0810001 (2024)
Jie Xu, Hui Liu*, Yue Shen, Guanxue Yang, Hao Zhou, and Siyuan Wang
Author Affiliations
  • School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu , China
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    DOI: 10.3788/CJL230989 Cite this Article Set citation alerts
    Jie Xu, Hui Liu, Yue Shen, Guanxue Yang, Hao Zhou, Siyuan Wang. Point Clouds Classification and Segmentation for Nursery Trees Based on Improved PointNet++ Model[J]. Chinese Journal of Lasers, 2024, 51(8): 0810001 Copy Citation Text show less
    Schematic diagram of feature fusion strategy
    Fig. 1. Schematic diagram of feature fusion strategy
    Acquisition of relative coordinates of neighboring points for each local area
    Fig. 2. Acquisition of relative coordinates of neighboring points for each local area
    Model combining coordinate attention mechanism and attentive pooling
    Fig. 3. Model combining coordinate attention mechanism and attentive pooling
    Classification branch of improved model
    Fig. 4. Classification branch of improved model
    Segmentation branch of improved model
    Fig. 5. Segmentation branch of improved model
    Nursery for data collection. (a) Whole scene of nursery; (b) part of scene of nursery
    Fig. 6. Nursery for data collection. (a) Whole scene of nursery; (b) part of scene of nursery
    Livox Horizon laser sensor and acquired point clouds. (a) Livox Horizon laser sensor; (b) acquired point clouds
    Fig. 7. Livox Horizon laser sensor and acquired point clouds. (a) Livox Horizon laser sensor; (b) acquired point clouds
    Seven common kinds of landscape trees. (a) Osmanthus fragrans; (b) Malus halliana; (c) cherry plum; (d) Acer palmatum; (e) Chimonanthus praecox; (f) loquat tree; (g) Chinese holly
    Fig. 8. Seven common kinds of landscape trees. (a) Osmanthus fragrans; (b) Malus halliana; (c) cherry plum; (d) Acer palmatum; (e) Chimonanthus praecox; (f) loquat tree; (g) Chinese holly
    Schematic diagram of segmented point clouds of seven tree species. (a) Osmanthus fragrans; (b) Malus halliana; (c) cherry plum; (d) Acer palmatum; (e) Chimonanthus praecox; (f) loquat tree; (g) Chinese holly
    Fig. 9. Schematic diagram of segmented point clouds of seven tree species. (a) Osmanthus fragrans; (b) Malus halliana; (c) cherry plum; (d) Acer palmatum; (e) Chimonanthus praecox; (f) loquat tree; (g) Chinese holly
    Confusion matrixes. (a) PointNet; (b) PointNet++; (c) ours
    Fig. 10. Confusion matrixes. (a) PointNet; (b) PointNet++; (c) ours
    Examples of visualization of segmentation results using PointNet, PointNet++, and proposed model (white boxes denote wrong predicted points). (a) Ground truth; (b) segmentation results of PointNet; (c) segmentation results of PointNet++; (d) segmentation results of proposed model
    Fig. 11. Examples of visualization of segmentation results using PointNet, PointNet++, and proposed model (white boxes denote wrong predicted points). (a) Ground truth; (b) segmentation results of PointNet; (c) segmentation results of PointNet++; (d) segmentation results of proposed model
    ParameterValue
    Detection range /m260
    Range error /cm2
    Angle error /(°)0.05
    Field-of-view (FOV) /(°)81.7×25.1
    Data rate /(point·s-1240000
    Table 1. Laser parameters
    CategoryNumber of treesNumber of point cloud groupsNumber of point cloud groups in training setNumber of point cloud groups in testing set
    Osmanthus fragrans12017912653
    Malus halliana14428320182
    Cherry plum16428720186
    Acer palmatum15129020387
    Chimonanthus praecox15829020387
    Loquat tree14621515164
    Chinese holly16328419985
    Table 2. Numbers of samples, point cloud groups, and groups in training and testing sets for different types of trees
    ParameterContent
    CPUIntel(R) Xeon(R) Gold 6226R
    GPUNVIDIA RTX3090
    CudaCuda11.7
    Data processing toolsPython3.8, PyCharm2020
    Deep learning frameworkPytorch
    Table 3. Software and hardware parameters for experiment
    ModelOAmAcc
    PointNet81.2580.00
    PointNet++89.7189.44
    Ours (SE)90.3392.39
    Ours (CBAM)90.8392.28
    Ours (CA)91.3393.13
    Ours92.5094.22
    Table 4. Testing results of classification on self-made dataset
    ModelmIoUPrecisionRecallF1 score
    PointNet76.9570.7176.0173.01
    PointNet++83.8585.3592.4088.47
    Ours (SE)87.0889.3294.8691.45
    Ours (CBAM)87.6188.5694.2991.17
    Ours (CA)89.8988.2994.5191.12
    Ours89.0990.0995.4492.59
    Table 5. Testing results of segmentation on self-made dataset
    Jie Xu, Hui Liu, Yue Shen, Guanxue Yang, Hao Zhou, Siyuan Wang. Point Clouds Classification and Segmentation for Nursery Trees Based on Improved PointNet++ Model[J]. Chinese Journal of Lasers, 2024, 51(8): 0810001
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