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Related Experiment Video

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YOLOv8A-SD: A Segmentation-Detection Algorithm for Overlooking Scenes in Pig Farms.

Yiran Liao1, Yipeng Qiu2, Bo Liu3

  • 1College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625000, China.

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Summary
This summary is machine-generated.

A new YOLOv8A-SD model enhances pig detection and counting accuracy in aerial farm surveillance. Optimal results were achieved by training with original images and using segmentation preprocessing during testing for practical deployment.

Keywords:
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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Artificial Intelligence

Background:

  • Accurate monitoring of livestock is crucial for efficient farm management.
  • Aerial surveillance presents unique challenges for object detection, especially in large-scale pig farming.
  • Existing detection models may struggle with variations in lighting, pig density, and aerial perspectives.

Purpose of the Study:

  • To develop and evaluate a refined object detection model for pig identification in aerial surveillance footage.
  • To improve the accuracy and efficiency of pig counting in complex farming environments.
  • To integrate detection and segmentation tasks for enhanced monitoring capabilities.

Main Methods:

  • Introduction of the YOLOv8A-SD model, incorporating the ADown attention mechanism.
  • Implementation of a dual-task strategy combining object detection and semantic segmentation.
  • Testing conducted on top-view footage from a large-scale pig farm in Sichuan, utilizing a substantial dataset for training and validation.

Main Results:

  • The YOLOv8A-SD model achieved high performance in detection tasks, with 96.1% Precision and 96.3% mAP50.
  • Strong segmentation performance was maintained, indicated by an 83.1% IoU (Intersection over Union).
  • Optimal results for counting accuracy (25.05 vs. actual 25.09 pigs) were obtained by training with original images and applying segmentation preprocessing during testing.

Conclusions:

  • The YOLOv8A-SD model demonstrates significant effectiveness for pig detection and counting in complex aerial surveillance scenarios.
  • The proposed method simplifies practical deployment by optimizing the training and testing preprocessing steps.
  • This research offers a reliable monitoring solution for intelligent farm management, enhancing operational efficiency and animal welfare.