In intensive farming, contactless individual pig identification is crucial for precision feeding and health monitoring. However, real-world barn conditions—such as fluctuating illumination, severe occlusions, non-rigid poses, and high inter-individual similarity—pose significant challenges. Existing models struggle to balance high accuracy with lightweight deployment. To address this, we propose YOLO-ESO, an optimized detection framework based on YOLOv10n. YOLO-ESO introduces three core innovations: (1) integrating the C2fODConv module into the backbone to strengthen feature learning under complex poses via dynamic convolution; (2) redesigning the neck with a Semantics and Detail Infusion (SDI) module to improve multi-scale fusion while suppressing background noise; and (3) embedding an Efficient Multi-Scale Attention (EMA) mechanism before the detection head to capture fine-grained identity cues like texture and contours. Evaluated on a real-world pig dataset, YOLO-ESO achieves an mAP@0. 5 of 96. 6%, an mAP@0. 5: 0. 95 of 71. 1%, and an F1 of 92. 0%. YOLO-ESO surpasses state-of-the-art detectors including YOLOv8, YOLOv11, and RT-DETR, while introducing only 8. 7 GFLOPs and 3. 48 million parameters. Overall, the proposed YOLO-ESO provides an accurate and lightweight solution for robust individual pig identification in complex farming environments, showing strong potential for practical deployment in precision livestock farming.
Juanhua et al. (Mon,) studied this question.