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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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A Sheep Behavior Recognition Approach Based on Improved FESS-YOLOv8n Neural Network.

Xiuru Guo^1,2, Chunyue Ma^1,2, Chen Wang^1,2

¹College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China.

Animals : an Open Access Journal From MDPI

|March 28, 2025

Summary

A new AI model, Fess-YOLOv8n, enhances sheep behavior detection for improved livestock health monitoring. This lightweight model offers better accuracy and real-time performance compared to traditional methods.

Keywords:

YOLOv8 behavior recognition lightweight object detection sheep

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Area of Science:

Computer Vision
Artificial Intelligence
Livestock Management

Background:

Sheep farming is crucial in Northern China, necessitating effective health monitoring.
Traditional sheep behavior detection methods (manual observation, contact devices) lack real-time performance and accuracy for large-scale operations.

Purpose of the Study:

To develop an advanced, lightweight sheep behavior detection model for enhanced health monitoring.
To improve the accuracy and real-time capabilities of automated sheep behavior analysis.

Main Methods:

Proposed Fess-YOLOv8n model, an enhanced YOLOv8n neural network.
Incorporated FasterNet structure and selective channel down-sampling (SCDown) for model lightweighting.
Integrated efficient multi-scale attention (EMA) and spatial channel synergistic attention (SCSA) modules to boost recognition performance.

Main Results:

The Fess-YOLOv8n model achieved a reduction of 2.56 MB in model size.
Demonstrated a 4.7% increase in detection accuracy on a self-built dataset.
Successfully provided technical support for large-scale sheep behavior detection.

Conclusions:

Fess-YOLOv8n offers a viable solution for accurate and efficient sheep behavior detection in large-scale farming.
The model lays a foundation for advanced sheep health monitoring systems through automated behavior analysis.