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A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++

文献类型： 外文期刊

作者： Chang, Kaixuan 1 ; Ma, Weihong 2 ; Xu, Xingmei 1 ; Qi, Xiangyu 3 ; Xue, Xianglong 2 ; Xu, Zhankang 2 ; Li, Mingyu 2 ; Guo, Yuhang 2 ; Meng, Rui 2 ; Li, Qifeng 1 ;

作者机构： 1.Jilin Agr Univ, Coll Informat & Technol, Changchun 130118, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

4.Natl Innovat Ctr Digital Seed Ind, Beijing 100097, Peoples R China

5.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

关键词： point cloud segmentation; PointNet++; 3D point cloud processing; SoftPool

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2024 年 14 卷 5 期

页码：

收录情况： SCI

摘要： In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig's behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model's ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model's segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds.

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