Treffer: Automated Cattle Head and Ear Pose Estimation Using Deep Learning for Animal Welfare Research.

Simple Summary: This study developed an AI system that uses deep learning (Mask R-CNN and FSA-Net) to automatically detect and estimate the 3D pose of cattle heads and ears from images, achieving high accuracy. The system enables non-invasive, real-time monitoring of cattle behavior, offering a practical alternative to invasive stress assessments. Designed for integration with farm cameras, it supports early stress detection and efficient management. Further research is needed to expand datasets, validate stress correlations, and optimize for mobile devices. With the increasing importance of animal welfare, behavioral indicators such as changes in head and ear posture are widely recognized as non-invasive and field-applicable markers for evaluating the emotional state and stress levels of animals. However, traditional visual observation methods are often subjective, as assessments can vary between observers, and are unsuitable for long-term, quantitative monitoring. This study proposes an artificial intelligence (AI)-based system for the detection and pose estimation of cattle heads and ears using deep learning techniques. The system integrates Mask R-CNN for accurate object detection and FSA-Net for robust 3D pose estimation (yaw, pitch, and roll) of cattle heads and left ears. Comprehensive datasets were constructed from images of Japanese Black cattle, collected under natural conditions and annotated for both detection and pose estimation tasks. The proposed framework achieved mean average precision (mAP) values of 0.79 for head detection and 0.71 for left ear detection and mean absolute error (MAE) of approximately 8–9° for pose estimation, demonstrating reliable performance across diverse orientations. This approach enables long-term, quantitative, and objective monitoring of cattle behavior, offering significant advantages over traditional subjective stress assessment methods. The developed system holds promise for practical applications in animal welfare research and real-time farm management. [ABSTRACT FROM AUTHOR]

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