A Lightweight Facial Landmark Recognition Model for Individual Sheep Based on SAMS-KLA-YOLO11

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an improved YOLO11 architecture, termed SAMS-KLA-YOLO11. The model incorporates a Sheep Adaptive Multi-Scale Convolution (SAMSConv) module to enhance feature extraction across breed-dependent facial scales, a Keypoint-Aware Lightweight Attention (KLAttention) mechanism to emphasize biologically discriminative facial landmarks, and the Efficient IoU (EIoU) loss to stabilize bounding box regression. A dataset of 3860 images from 68 individuals belonging to three breeds (Hu, Dorper, and Dorper × Hu crossbreeds) was collected under unconstrained farm conditions and annotated with five facial keypoints. On this dataset, the proposed model achieves higher precision, recall, and mAP than several mainstream YOLO-based baselines, while reducing FLOPs and parameter count compared with the original YOLO11. Additional ablation experiments confirm that each proposed module provides complementary benefits, and OKS-based evaluation shows accurate facial keypoint localization. All results are obtained on a single, site-specific dataset without external validation or on-device deployment benchmarks, so the findings should be viewed as an initial step toward practical sheep face recognition rather than definitive evidence of large-scale deployment readiness.