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Mounting Behaviour Recognition for Pigs Based on Deep Learning.

Dan Li1, Yifei Chen1, Kaifeng Zhang1

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)
|November 16, 2019
PubMed
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This study introduces an efficient algorithm using Mask Region-Convolutional Neural Networks (Mask R-CNN) and kernel extreme learning machines (KELM) to detect pig mounting behavior from images. The developed method accurately identifies mounting, improving animal welfare by enabling early intervention.

Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Mounting behavior in pigs can lead to injuries like wounds and fractures, negatively impacting animal welfare.
  • Accurate detection of mounting behavior is crucial for timely intervention and welfare improvement in both commercial and experimental settings.

Purpose of the Study:

  • To develop an efficient learning algorithm for detecting pig mounting behavior using visible light images.
  • To address challenges in pig segmentation caused by occlusion and similar body-background colors.

Main Methods:

  • Utilized Mask Region-Convolutional Neural Networks (Mask R-CNN) for pig segmentation and feature extraction.
  • Employed a kernel extreme learning machine (KELM) for classifying extracted eigenvectors to identify mounting behavior.
Keywords:
Mask R-CNNdeep learningkernel-extreme learning machinemounting behaviourpig

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  • Collected data from four Göttingen minipigs over one week, classifying frames into positive (mounting) and negative (non-mounting) samples.

    • Main Results:

    • Achieved high accuracy in pig segmentation with 94.92% accuracy and 0.8383 mean pixel accuracy (MPA).
    • Demonstrated high overall performance in detecting mounting behavior with 91.47% accuracy, 95.2% sensitivity, 88.34% specificity, and a 0.8324 Matthews correlation coefficient.
    • Successfully handled segmentation difficulties arising from partial occlusion and adhesion of pig bodies.

    Conclusions:

    • The developed algorithm provides an efficient and accurate method for recognizing pig mounting behavior from visible light images.
    • This approach offers a robust solution for pig segmentation, even in challenging visual conditions, thereby enhancing animal welfare monitoring.