Improved RT-DETR Combined with Digital Twin for Accurate Posture Detection of Sows

Precise monitoring of sow behaviour is essential for enhancing animal welfare and production efficiency in precision husbandry. This study proposes an improved RT-DETR model to address real-time detection challenges in complex farming environments. By integrating innovative multi-scale feature fusion and lightweight attention mechanisms, the model achieves high-precision detection of four key postures (standing, sitting, sternal recumbency, and lateral recumbency). Experimental results show that the model attains an mAP@0.5 of 96.6% and a processing speed of 56 FPS, significantly outperforming existing methods. Furthermore, a Unity3D-based digital twin system was constructed to enable real-time bidirectional mapping, achieving a low latency of 320 ms. This system proposes a potential technical framework for intelligent pig farm management, providing a reliable tool for automated welfare assessment and operational decision support.