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Multi-Pig Part Detection and Association with a Fully-Convolutional Network.

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  • 1Department of Electrical and Computer Engineering, University of Nebraska⁻Lincoln, Lincoln, NE 68505, USA. epsota@unl.edu.

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This study introduces a new dataset and computer vision method for automated pig detection in group housing. The system achieves high accuracy, aiding livestock monitoring.

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Animal science

Background:

  • Automated livestock monitoring using computer vision is challenging due to limited public datasets.
  • Existing solutions often rely on small, private datasets for specific tasks.
  • A need exists for robust, generalizable methods for detecting multiple animals in group settings.

Purpose of the Study:

  • To develop and evaluate a novel computer vision method for instance-level detection and orientation of multiple pigs in group-housed environments.
  • To introduce a comprehensive, publicly available dataset for pig detection research.
  • To assess the performance and robustness of the proposed detection method.

Main Methods:

  • A single fully-convolutional neural network was employed for detecting pig location and orientation.
  • The method represents animal body parts and pairwise associations within the image space.
  • A new dataset of 2000 annotated images featuring 24,842 individual pigs across 17 locations was created.

Main Results:

  • The proposed method achieved over 99% precision and 96% recall on previously seen environments.
  • Testing on unseen environments and lighting conditions yielded 91% precision and 67% recall, demonstrating robustness.
  • The accompanying dataset is publicly accessible for research purposes.

Conclusions:

  • The developed computer vision method and dataset significantly advance automated monitoring capabilities for pigs in group housing.
  • The findings highlight the potential for accurate, non-invasive livestock surveillance.
  • Public availability of the dataset will foster further research and development in agricultural computer vision.