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Related Experiment Video

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DGS-YOLO: A Detection Network for Rapid Pig Face Recognition.

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|January 28, 2026
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Summary

This study introduces DGS-YOLO, an enhanced pig facial recognition model for improved food safety and insurance accuracy. The model achieves superior performance in complex farming environments, even with limited data.

Keywords:
YOLOv11facial recognitionfeature extraction

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Animal Science

Background:

  • Facial recognition for pigs is crucial for food safety and insurance.
  • Existing methods struggle with accuracy in complex farming environments due to occlusions and similar textures.

Purpose of the Study:

  • To develop an enhanced facial recognition model, DGS-YOLO, for precise identification of group-raised young pigs.
  • To improve recognition accuracy in challenging agricultural settings.

Main Methods:

  • Proposed DGS-YOLO model based on YOLOv11n, incorporating dynamic convolutions (DMConv), a C3k2_GBC module, SimAM attention, and Shape-IoU loss.
  • Utilized self-built datasets for training and evaluation.

Main Results:

  • DGS-YOLO demonstrated improvements of 4% in accuracy, 2.1% in recall, and 2.3% in mAP50 over the YOLOv11n baseline.
  • Outperformed Faster R-CNN and SSD in comprehensive metrics.
  • Showcased strong generalization with significant accuracy and mAP50 increases (20.1% and 10.3%) in limited sample scenarios.

Conclusions:

  • DGS-YOLO provides a highly accurate and robust solution for pig facial recognition in complex environments.
  • The enhanced model addresses practical demands in the food safety and insurance sectors.