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Related Experiment Video

Updated: Jul 25, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Sheep Face Recognition Model Based on Deep Learning and Bilinear Feature Fusion.

Zhuang Wan1, Fang Tian1,2,3, Cheng Zhang1,2,3

  • 1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Animals : an Open Access Journal From MDPI
|June 28, 2023
PubMed
Summary

Accurate sheep identification is crucial for digital farming. A new deep learning model using RepVGG and bilinear features enhances sheep face recognition, achieving over 99% accuracy despite pose variations.

Keywords:
RepVGGdeep learningfeature fusionsheep face recognition

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Animal Science

Background:

  • Accurate individual sheep identification is essential for digitalized sheep farming and precision animal husbandry.
  • Challenges in sheep face recognition include variations in posture and shooting angles, impacting accuracy.

Purpose of the Study:

  • To develop a robust deep learning model for accurate sheep face recognition.
  • To mitigate the impact of pose and angle variations on recognition accuracy.

Main Methods:

  • Proposed a deep learning model integrating RepVGG algorithm and bilinear feature extraction and fusion.
  • Designed a feature extraction channel with an attention mechanism and RepVGG blocks for efficient, lossless model compression.
  • Utilized a bilinear feature extraction network and feature fusion at the same scale to enhance recognition robustness.

Main Results:

  • The model achieved high recognition rates: 95.95% (side-face), 97.64% (front-face), and 99.43% (full-face).
  • Demonstrated significant reduction in the effect of sheep face pose on recognition accuracy.
  • Outperformed several existing state-of-the-art sheep face recognition models.

Conclusions:

  • The proposed deep learning model effectively addresses challenges in sheep face recognition caused by pose and angle variations.
  • This approach enhances individual sheep identification, supporting advancements in precision animal husbandry and digital sheep farming.