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DESA-YOLO: A Growth-Stage Adaptive Pig Face Recognition Algorithm Based on Multi-Scale Feature Fusion
Xin Li1, Jinghan Cai1, Tonghai Liu2
1College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China.
Animals : an Open Access Journal From MDPI
|May 27, 2026
Summary
This study introduces DESA-YOLO, an improved pig face recognition algorithm, enhancing adaptability across different growth stages. The novel approach boosts precision and recall for efficient, accurate pig identification in farming.
Area of Science:
- Agricultural Technology
- Computer Vision
- Animal Science
Background:
- Traditional pig identification methods are inefficient and costly.
- Pig face recognition offers potential for precision farming and disease prevention.
- Facial features vary significantly across pig growth stages, posing a recognition challenge.
Purpose of the Study:
- To develop an adaptable pig face recognition algorithm for different growth stages.
- To improve the accuracy and efficiency of individual pig identification.
- To address the limitations of existing pig recognition technologies.
Main Methods:
- An improved YOLO11 architecture named DESA-YOLO was proposed.
- Key innovations include DualConv structure, EMA module, SEAM attention mechanism, and ASFF detection head.
- The model was evaluated against YOLOv5 and YOLOv8, with ablation studies and heatmap visualizations.
Main Results:
- DESA-YOLO achieved a 93.7% mAP, outperforming baseline YOLO11 by 3%.
- Significant improvements were observed in precision (6.3%), recall (3.5%), and F1 score.
- The model demonstrated enhanced adaptability and stability across various pig growth stages.
Conclusions:
- The proposed DESA-YOLO algorithm effectively addresses pig face recognition challenges across growth stages.
- The integrated modules enhance detection accuracy and model adaptability.
- DESA-YOLO offers a viable, real-time solution for precision pig farming.
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