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Grazing Sheep Behaviour Recognition Based on Improved YOLOV5.

Tianci Hu1,2, Ruirui Yan3, Chengxiang Jiang1,2

  • 1College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

Sensors (Basel, Switzerland)
|July 11, 2023
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Summary

This study introduces an enhanced You Only Look Once Version 5 (YOLOV5) algorithm for recognizing sheep behavior in pastures. The improved model achieves over 90% accuracy, aiding precision livestock management.

Keywords:
behaviour recognitiongrazing sheepimproved YOLOV5pasture

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Area of Science:

  • Computer Vision
  • Animal Behaviour
  • Precision Livestock Management

Background:

  • Sheep behavior monitoring is crucial for physiological health assessment but challenging in grazing environments due to variable conditions.
  • Accurate recognition of sheep behavior in free-range settings requires robust algorithms addressing lighting and environmental variations.

Purpose of the Study:

  • To develop and evaluate an enhanced sheep behavior recognition algorithm using the You Only Look Once Version 5 (YOLOV5) model.
  • To investigate the impact of different shooting methodologies and environmental conditions on model performance and generalization.
  • To propose a system design for real-time sheep behavior recognition in practical applications.

Main Methods:

  • Construction of sheep behavior datasets utilizing two distinct shooting methodologies.
  • Application and enhancement of the YOLOV5 model, including the integration of an attention mechanism module.
  • Cross-validation techniques to assess the model's generalization ability across different environmental conditions.
  • Design of a cloud-based structure incorporating the Real-Time Messaging Protocol (RTMP) for real-time video stream processing.

Main Results:

  • The YOLOV5 model achieved an average accuracy exceeding 90% for three behavior classifications on the developed datasets.
  • Cross-validation indicated that models trained with handheld camera data exhibited superior generalization capabilities.
  • The enhanced YOLOV5 model with an attention mechanism achieved a mean Average Precision (mAP@0.5) of 91.8%, a 1.7% improvement.

Conclusions:

  • The study successfully developed an improved YOLOV5 algorithm for accurate sheep behavior recognition in pasture settings.
  • The proposed model effectively detects daily sheep behaviors, offering significant potential for precision livestock management and modern husbandry.
  • The real-time recognition system design provides a pathway for practical implementation in agricultural environments.