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An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model.

Yigui Huang1,2, Deqin Xiao1,2, Junbin Liu1,2

  • 1College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.

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Summary
This summary is machine-generated.

This study introduces an improved pig counting algorithm (MPC-YD) using YOLOv5x and DeepSORT for efficient and accurate automated pig counting in agriculture. The new method enhances detection and tracking, showing high precision in real-world scenarios.

Keywords:
computer visionmultiobject trackingobject detectionpig

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Animal Science

Background:

  • Manual pig counting is inefficient, costly, and hinders statistical analysis.
  • Existing pig video tracking faces challenges in feature detection, tracking loss due to rapid movement, and counting deviations.

Purpose of the Study:

  • To develop an improved pig counting algorithm (MPC-YD) addressing limitations in current automated systems.
  • To enhance the accuracy and efficiency of pig counting in both breeding and slaughterhouse environments.

Main Methods:

  • An enhanced YOLOv5x model incorporating SPP networks of two sizes and SoftPool operations for improved pig part detection.
  • Integration of a pig re-identification network and spatial state correction for accurate pig tracking.
  • Development of a frame number judgment method within DeepSORT for precise counting.

Main Results:

  • The MPC-YD algorithm achieved 99.24% average precision in pig object detection.
  • Demonstrated 85.32% accuracy in multi-target pig tracking.
  • Achieved a 98.14% correlation coefficient (R² ) for video-based pig counting in slaughterhouse aisles, with stable performance in breeding environments.

Conclusions:

  • The MPC-YD algorithm significantly improves pig counting accuracy and efficiency compared to existing methods.
  • The algorithm shows strong applicability and potential for real-world pig farming and management.
  • This advanced system offers a viable solution for automated livestock monitoring.