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SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.

Manu Ramesh1, Amy R Reibman1

  • 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
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SURABHI enhances deep-learning keypoint detection by generating challenging training instances, improving cattle identification accuracy. This self-training scheme boosts cow recognition, especially with limited data.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Animal Science

Background:

  • Accurate keypoint detection is crucial for animal identification systems.
  • Deep learning models often require extensive labeled data for optimal performance.
  • Existing methods may struggle with minimal training datasets.

Purpose of the Study:

  • To introduce SURABHI, a self-training scheme for enhancing deep-learning keypoint detection.
  • To improve keypoint detection accuracy by generating effective, machine-annotated 'hard' instances.
  • To boost the performance of cattle identification systems, particularly the Eidetic Cattle Recognition System.

Main Methods:

  • Developed SURABHI, a self-training methodology for generating machine-annotated instances.
  • Focused on creating 'hard' instances that challenge keypoint detection models.
Keywords:
cattle recognitionhard instanceskeypoint detectionself-training

Related Experiment Videos

  • Engineered the scheme for predicting cattle keypoints from a top-down view.
  • Integrated SURABHI with the Eidetic Cattle Recognition System.

    • Main Results:

    • SURABHI significantly improved keypoint detection accuracy without altering model architecture.
    • Achieved a top-6 cow recognition accuracy of 91.89% on a cow video dataset.
    • Increased the number of correctly identified cow instances by 22% compared to fully supervised training.
    • Demonstrated substantial accuracy gains, especially with minimal available training data.

    Conclusions:

    • SURABHI is an effective self-training scheme for improving deep-learning keypoint detection.
    • The method enhances cattle identification accuracy, proving valuable for systems like Eidetic Cattle Recognition.
    • SURABHI offers a robust solution for scenarios with limited training data, outperforming traditional supervised methods.