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Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks.

Nina Volkmann^1,2, Claudius Zelenka³, Archana Malavalli Devaraju³

¹Science and Innovation for Sustainable Poultry Production (WING), University of Veterinary Medicine Hannover, Foundation, 49377 Vechta, Germany.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

This study developed a camera system using neural networks to detect injurious pecking in turkeys. Keypoint detection accurately identified individual birds and injury locations, improving animal welfare monitoring.

Keywords:

animal welfare crowded dataset injury location keypoint detection pose estimation turkeys

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Area of Science:

Animal Science
Computer Vision
Artificial Intelligence

Background:

Injurious pecking is a significant welfare and economic issue in turkey farming.
Early detection of injuries is crucial to prevent escalation and reduce losses.

Purpose of the Study:

To develop an automated camera-based system for monitoring turkey flocks and detecting injuries.
To utilize neural networks for precise injury detection and localization.

Main Methods:

Applied a keypoint detection model to identify seven turkey keypoints across 244 images (7660 birds).
Compared two state-of-the-art pose estimation approaches.
Integrated a refined keypoint detection model (HRNet-W48) with a segmentation model for injury detection and classification (e.g., 'near tail', 'near head').

Main Results:

Keypoint detection effectively differentiated individual turkeys, even in crowded conditions.
The combined model successfully detected and localized injuries.
Demonstrated potential for classifying injury locations relative to defined keypoints.

Conclusions:

Keypoint detection models show promise for individual animal identification in flock monitoring.
The integrated system offers a viable solution for automated detection of injurious pecking in turkeys.
This technology can significantly enhance animal welfare and reduce economic losses in poultry farming.