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Updated: Jun 27, 2025

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Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT.

Xiaobao Shao1, Chengcheng Liu1, Zhixuan Zhou1

  • 1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Animals : an Open Access Journal From MDPI
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved YOLOv7 model with DeepSORT for accurate, real-time pig counting in complex environments. The enhanced model achieves high accuracy and speed, crucial for automated large-scale farming.

Keywords:
DeepSORTPConvYOLOv7attention mechanismsdynamic countingpig counting

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Agricultural Technology

Background:

  • Accurate pig inventory is essential for precision agriculture.
  • Automated counting in complex pigsties is challenging due to obstructions and pig behavior.
  • Existing deep learning methods often rely on static images or overhead views, limiting real-world application.

Purpose of the Study:

  • To develop a robust video-based dynamic counting method for pigs in complex environments.
  • To enhance the YOLOv7 object detection model for improved accuracy and efficiency in pig counting.
  • To integrate YOLOv7 with DeepSORT for real-time tracking and counting.

Main Methods:

  • Optimized YOLOv7 architecture using PConv for reduced computation and improved inference speed.
  • Incorporated coordinate attention (CA) mechanism to enhance perception at oblique angles and robustness.
  • Combined the enhanced YOLOv7 with DeepSORT for video-based dynamic pig counting.

Main Results:

  • The improved YOLOv7 achieved higher mAP across oblique, overhead, and combined datasets compared to the original model.
  • Demonstrated superior performance against YOLOv5, YOLOv4, YOLOv3, Faster RCNN, and SSD in target detection.
  • The YOLOv7-DeepSORT system achieved an average accuracy of 96.58% with 22 FPS in dynamic counting experiments.

Conclusions:

  • The proposed dynamic counting method effectively addresses the challenges of automated pig counting in complex environments.
  • The enhanced YOLOv7-DeepSORT model offers a viable solution for real-time, accurate pig inventory in large-scale farming.
  • This research provides valuable data and a reference for advancing automated pig counting technologies.