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

Kaixuan Chang, Weihong Ma (), Xingmei Xu, Xiangyu Qi, Xianglong Xue, Zhankang Xu, Mingyu Li, Yuhang Guo, Rui Meng and Qifeng Li ()
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Kaixuan Chang: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Weihong Ma: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Xingmei Xu: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Xiangyu Qi: National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
Xianglong Xue: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Zhankang Xu: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Mingyu Li: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Yuhang Guo: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Rui Meng: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Qifeng Li: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2024, vol. 14, issue 5, 1-19

Abstract: 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.

Keywords: point cloud segmentation; PointNet++; 3D point cloud processing; SoftPool (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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