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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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SheepFaceNet: A Speed-Accuracy Balanced Model for Sheep Face Recognition.

Xiaopeng Li1, Yichi Zhang1, Shuqin Li1

  • 1College of Information Engineering, Northwest A&F University, Xianyang 712100, China.

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

A new lightweight model, SheepFaceNet, enhances sheep face recognition for smart farming. It balances speed and accuracy, improving individual sheep identification efficiency.

Keywords:
convolutional neural networklightweight modelreparameterizationsheep face recognitionspeed-accuracy trade-off

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Agricultural Technology

Background:

  • Individual sheep identification is crucial for smart farming, but current computer vision models are slow and difficult to deploy.
  • Existing sheep face recognition systems face challenges with large parameter sizes and slow processing speeds.

Purpose of the Study:

  • To develop an efficient and fast lightweight sheep face recognition model for smart farming applications.
  • To address the limitations of current models in terms of speed, accuracy, and deployment.

Main Methods:

  • Proposed an efficient and fast basic module (Eblock) to build the SheepFaceNet model.
  • Developed SheepFaceNetDet for detection using Eblock, BiFPN, and optimized network structures.
  • Developed SheepFaceNetRec for recognition using Eblock, ECA channel attention, and multi-scale feature fusion.

Main Results:

  • SheepFaceNet achieved a recognition speed of 387 sheep face images per second.
  • The model demonstrated a high accuracy rate of 97.75% on a self-built dataset.
  • Achieved an optimal balance between recognition speed and accuracy.

Conclusions:

  • SheepFaceNet offers an efficient and accurate solution for sheep face recognition.
  • The model's lightweight design facilitates deployment in smart farming environments.
  • This research is expected to advance the adoption of deep learning in sheep production.