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Related Experiment Videos

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
PubMed
Summary
This summary is machine-generated.

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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.
Keywords:
YOLO11growth stagesindividual recognitionpig face recognition

Related Experiment Videos

  • 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.