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This study introduces a novel method for pig pen monitoring that improves detection accuracy and reduces latency by using static camera information. The approach enhances deep learning models for better performance in unseen environments without retraining.

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Area of Science:

  • Computer Vision
  • Animal Science
  • Machine Learning

Background:

  • Deep learning detectors face accuracy-latency trade-offs and performance drops in new environments.
  • Fixed-camera pig pen monitoring presents unique static characteristics that can be leveraged.

Purpose of the Study:

  • To enhance both accuracy and latency in deep learning-based pig monitoring systems.
  • To address performance degradation in unseen environments without model retraining.

Main Methods:

  • Utilized static background and infrastructure information via a one-time preprocessing step.
  • Introduced Background-suppressed Image Generator (BIG), Facility Image Generator (FIG), and Background Suppression Integration (BSI) modules.
  • Employed difference-aware fusion with 3D convolution for efficient feature integration and domain gap reduction.

Main Results:

  • Improved AP50 accuracy from 75% to 86% on the German pig dataset.
  • Reduced latency on Jetson Orin Nano from 67 ms to 41 ms.
  • Demonstrated effective performance on an unseen Korean Hadong pig dataset.

Conclusions:

  • The proposed method significantly enhances detection accuracy and operational efficiency for pig monitoring.
  • Leveraging static environmental features offers a robust solution for deep learning models in dynamic settings.
  • The approach effectively bridges the domain gap, enabling better generalization to unseen environments.