College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China.
Sensors (Basel). 2023 May 28;23(11):5156. doi: 10.3390/s23115156.
The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig's back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig's back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.
猪的个体识别是精准养殖（PLF）的基础，可以为个性化饲养、疾病监测、生长状况监测和行为识别提供前提。猪脸识别存在猪脸样本难以采集、图像易受环境和身体污垢影响等问题。针对这些问题，我们提出了一种利用猪背部三维（3D）点云进行个体识别的方法。首先，建立了一种基于 PointNet++算法的点云分割模型，用于从复杂背景中分割猪背部点云，并将其作为个体识别的输入。然后，通过增加自适应全局采样半径、加深网络结构和增加特征数量，构建了一种基于改进的 PointNet++LGG 算法的个体猪识别模型，以提取更高维的特征，从而准确识别具有相似体型的不同个体。总共采集了 10 头猪的 10574 个 3D 点云图像来构建数据集。实验结果表明，基于 PointNet++LGG 算法的个体猪识别模型的准确率达到 95.26%，分别比 PointNet 模型、PointNet++SSG 模型和 MSG 模型提高了 2.18%、16.76%和 17.19%。基于猪背部 3D 点云的个体猪识别是有效的。这种方法易于与身体状况评估和行为识别等功能集成，有利于精准养殖的发展。
