# DK-EffiPointMLP: An Efficient 3D Dorsal Point Cloud Network for Individual Identification of Pigs

## TL;DR

This paper introduces a new 3D point cloud model for accurately identifying individual pigs in farms, improving automation and animal welfare.

## Contribution

The novel DK-EffiPointMLP model integrates dual-branch and efficient modules for enhanced pig identification accuracy.

## Key findings

- DK-EffiPointMLP achieved 96.86% accuracy on a dataset of 8411 pig samples.
- The model outperformed existing methods by 2.74 percentage points on the self-built dataset.
- It also showed 95.2% accuracy on the ModelNet40 benchmark dataset.

## Abstract

This study addresses the challenge of non-contact individual pig identification in precision livestock farming. We developed a 3D point cloud recognition model based on PointMLP, enhancing its feature extraction and information processing capabilities. Our model outperforms existing methods, achieving 96.86% accuracy on a dataset of 8411 samples from 10 pigs. This solution enables automated, individual-level management in commercial farms, improving operational efficiency and supporting animal welfare.

Accurate non-contact individual identification of pigs is crucial for their intelligent and efficient management. However, traditional recognition technologies generally suffer from weak local feature expression, feature redundancy, and insufficient channel importance modeling. To address these challenges, this study proposes a novel network model, DK-EffiPointMLP, for individual identification based on 3D dorsal point clouds. The model integrates a Dual-branch Local Feature enhancement module (DLF) and an Efficient Partial Convolution-Residual Refinement module (EffiConv). Specifically, the DLF module adopts a dual-branch structure of KNN and dilated KNN to expand the receptive field, while the EffiConv module combines 1D convolution with the SE mechanism to strengthen key channel modeling. To evaluate the model, a dataset of 10 individual pigs with 8411 samples was constructed. Experimental results show that DK-EffiPointMLP achieves accuracies of 96.86% on this self-built dataset and 95.2% on ModelNet40. When re-training all baseline models under the same pipeline and preprocessing protocols, our model outperformed existing mainstream models by 2.74 and 1.1 percentage points, respectively. This approach provides an efficient solution for automated management in commercial farming.