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Research on Instance Segmentation Algorithm for Caged Chickens in Infrared Images Based on Improved Mask R-CNN.

Youqing Chen1,2, Hang Liu2,3, Lun Wang1,2

  • 1College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China.

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Summary

This study introduces an improved Mask R-CNN algorithm for segmenting caged chickens in infrared images, enhancing health monitoring in large-scale farming. The new method significantly boosts detection and segmentation accuracy, even in crowded conditions.

Keywords:
AC-FPNCBAMMask R-CNNcaged chickensinstance segmentation

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Animal Science

Background:

  • Infrared imaging offers insights into chicken health.
  • Accurate detection and segmentation of individual chickens are crucial for large-scale farming.
  • Obstacles and chicken clustering in infrared images pose segmentation challenges.

Purpose of the Study:

  • To develop an advanced instance segmentation algorithm for detecting and segmenting caged chickens in infrared images.
  • To improve feature extraction and segmentation accuracy in challenging high-density environments.
  • To enhance automated health monitoring and intelligent breeding management.

Main Methods:

  • A Mask R-CNN-based instance segmentation algorithm was proposed.
  • The backbone network was enhanced with CBAM (Convolutional Block Attention Module).
  • The algorithm was combined with the AC-FPN (Attentive Cross-scale Feature Pyramid Network) architecture.

Main Results:

  • The enhanced model achieved 78.66% AP and 85.80% AR10 for object detection (COCO metrics).
  • Segmentation tasks yielded 73.94% AP and 80.42% AR10, improving by 32.91% and 17.78% over the original model.
  • The 'Chicken-many' category reached 98.51% segmentation accuracy, with other categories exceeding 93%.

Conclusions:

  • The proposed instance segmentation method effectively recognizes and segments caged chickens in challenging infrared imaging conditions.
  • This advancement supports enhanced production efficiency and intelligent breeding management in high-density poultry farming.
  • The improved accuracy in detecting and segmenting chickens contributes to better health monitoring systems.