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Enhancing Pig Behavior Recognition in Complex Environments: A Transfer Learning-Assisted YOLO11 Network with Wavelet

Taoyang Wang1, Yu Hu1, Hua Yin1

  • 1School of Software, Jiangxi Agricultural University, Nanchang 330045, China.

Animals : an Open Access Journal From MDPI
|March 28, 2026
PubMed
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This study introduces optimized YOLOv11n for efficient pig behavior recognition, enhancing disease detection and animal welfare in precision agriculture. The lightweight model achieves high accuracy with fewer parameters, enabling real-time applications.

Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Animal Science

Background:

  • Accurate pig behavior recognition is crucial for disease detection, animal welfare, and precision agriculture.
  • Existing deep learning models are often too complex or resource-intensive for practical deployment in farming environments.
  • There is a need for efficient and generalizable models for real-time pig behavior analysis on resource-limited devices.

Purpose of the Study:

  • To develop a lightweight and efficient deep learning model for pig behavior recognition.
  • To enhance the performance of the YOLOv11n model through targeted optimizations.
  • To enable real-time pig behavior detection for smart livestock management.

Main Methods:

  • Proposed three optimizations for the lightweight YOLOv11n model: SCSA-CBAM for feature discrimination, WFU for cross-scale integration, and WTConv for reduced computational overhead.
Keywords:
YOLO11attention mechanismlightweight detectionpig behavior recognitiontransfer learning

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  • Employed a two-stage transfer learning approach with data augmentation, initialized with COCO pre-trained weights.
  • Evaluated the model on a self-built six-category pig behavior dataset (2480 images).

    • Main Results:

    • The optimized YOLOv11n-SCSA-WFU-WT model achieved high performance metrics (mAP@0.5: 0.974, mAP@0.5:0.95: 0.785).
    • The model demonstrates a favorable accuracy-efficiency trade-off with 3.40 M parameters, 7.8 GFLOPs, and 72.28 FPS.
    • Ablation studies confirmed the effectiveness of each proposed module, showing substantial improvements over the baseline.

    Conclusions:

    • The proposed optimized YOLOv11n model effectively facilitates real-time pig behavior detection.
    • The method offers a lightweight yet accurate solution suitable for resource-limited smart livestock management systems.
    • The advancements contribute to improved early disease detection and animal welfare monitoring in pigs.