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A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet

文献类型： 外文期刊

作者： Deng, Qiaomei 1 ; Zhao, Junhong 2 ; Li, Rui 1 ; Liu, Genhua 3 ; Hu, Yaowen 4 ; Ye, Ziqing 3 ; Zhou, Guoxiong 3 ;

作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Math, Changsha 410004, Peoples R China

2.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

3.Cent South Univ Forestry & Technol, Coll Elect Informat & Phys, Changsha 410073, Peoples R China

4.Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China

关键词： pumpkin seedling; point cloud; stem segmentation; CPHNet; CRA-MLP; PESA; HCE-dice loss

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2024 年 13 卷 16 期

页码：

收录情况： SCI

摘要： Accurate segmentation of the stem of pumpkin seedlings has a great influence on the modernization of pumpkin cultivation, and can provide detailed data support for the growth of pumpkin plants. We collected and constructed a pumpkin seedling point cloud dataset for the first time. Potting soil and wall background in point cloud data often interfere with the accuracy of partial cutting of pumpkin seedling stems. The stem shape of pumpkin seedlings varies due to other environmental factors during the growing stage. The stem of the pumpkin seedling is closely connected with the potting soil and leaves, and the boundary of the stem is easily blurred. These problems bring challenges to the accurate segmentation of pumpkin seedling point cloud stems. In this paper, an accurate segmentation algorithm for pumpkin seedling point cloud stems based on CPHNet is proposed. First, a channel residual attention multilayer perceptron (CRA-MLP) module is proposed, which suppresses background interference such as soil. Second, a position-enhanced self-attention (PESA) mechanism is proposed, enabling the model to adapt to diverse morphologies of pumpkin seedling point cloud data stems. Finally, a hybrid loss function of cross entropy loss and dice loss (HCE-Dice Loss) is proposed to address the issue of fuzzy stem boundaries. The experimental results show that CPHNet achieves a 90.4% average cross-to-merge ratio (mIoU), 93.1% average accuracy (mP), 95.6% average recall rate (mR), 94.4% F1 score (mF1) and 0.03 plants/second (speed) on the self-built dataset. Compared with other popular segmentation models, this model is more accurate and stable for cutting the stem part of the pumpkin seedling point cloud.

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