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Related Experiment Videos

Pig Passage Counting Based on Improved YOLO and HMTC Strategy.

Lu Yang1,2, Saisai Wu3, Shuqing Han1,2

  • 1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Animals : an Open Access Journal From MDPI
|July 15, 2026
PubMed
Summary

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Optimal Foraging00:48

Optimal Foraging

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Accurate pig counting is crucial for livestock management. This study introduces an improved YOLO model and a Hysteresis-based Multi-frame Temporal Confirmation Counting Strategy (HMTC) for precise, real-time pig passage counting in swine production.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Accurate pig counting is essential for efficient large-scale swine production and livestock management.
  • Existing automated counting methods face challenges with bidirectional passages, boundary oscillations, and occlusion in real-world corridor environments.

Purpose of the Study:

  • To develop and validate an integrated system for accurate pig passage counting in swine production.
  • To enhance object detection models for improved accuracy and efficiency in livestock environments.
  • To implement a robust counting strategy that overcomes limitations of existing methods in complex scenarios.

Main Methods:

  • An improved YOLOv11s detection model was developed using RepViT blocks, dynamic upsampling (DySample), and shape-aware bounding box regression (Shape-IoU).
Keywords:
YOLOv11livestock managementobject detectionpig counting

Related Experiment Videos

  • A Hysteresis-based Multi-frame Temporal Confirmation Counting Strategy (HMTC) was integrated to handle bidirectional passages and reduce counting errors.
  • The system was evaluated on nine videos from a single transfer corridor, assessing detection accuracy, parameter efficiency, and real-time performance.

    • Main Results:

    • The enhanced YOLO model achieved a mean Average Precision (mAP50) of 0.982 with 8.28M parameters, a 12.3% reduction from the baseline, while improving detection.
    • The integrated HMTC system demonstrated an overall counting accuracy of 99.21% on the test set.
    • The system achieved real-time performance exceeding 30 FPS on an embedded edge device without compromising counting accuracy.

    Conclusions:

    • The proposed integrated system, combining an enhanced YOLO model and HMTC strategy, provides a cohesive and accurate solution for pig passage counting.
    • The system effectively addresses challenges like bidirectional flow and boundary oscillations, offering a promising foundation for automated animal inventory management.
    • Further validation across diverse farm environments is recommended to broaden the applicability of this automated livestock management technology.