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Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks
Nina Volkmann1,2, 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 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.
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.
