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A lightweight cow mounting behavior recognition system based on improved YOLOv5s.

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Area of Science:

  • Computer Vision
  • Animal Behavior Analysis
  • Machine Learning

Background:

  • Accurate detection of cow mounting behavior is crucial for livestock management.
  • Existing methods often struggle with speed and model size in dense environments.

Purpose of the Study:

  • To develop a lightweight and rapid detection system for cow mounting behavior.
  • To enhance detection speed and model efficiency in dense scenes.

Main Methods:

  • Designed a lightweight backbone network using EfficientNetV2 concepts, incorporating attention mechanisms, inverted residual structures, and depth-wise separable convolutions.
  • Developed a feature enhancement module utilizing residual structures, efficient attention mechanisms, and Ghost convolution.
  • Integrated the lightweight backbone and feature enhancement module with YOLOv5s to create the cow mounting behavior recognition model.

Main Results:

  • The proposed model achieved an inference speed of 333.3 frames per second (fps) with an inference time of 4.1 milliseconds per image.
  • The model demonstrated a mean Average Precision (mAP) of 87.7%, outperforming YOLOv5s by 2.1%.
  • The new model is 0.47 times faster and has 2.34 times less weight compared to YOLOv5s.

Conclusions:

  • The developed lightweight rapid recognition model accurately detects cow mounting behavior in dense scenes.
  • The system's high inference speed and reduced model weight are beneficial for all-weather, real-time monitoring in large cattle farms.