Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex...

Intelligent management of large-scale farms necessitates efficient monitoring of individual livestock. To address this need, a three-phase intelligent monitoring system based on deep learning was designed, integrating a multi-part detection network for flock inventory counting, a facial classification model for facial identity recognition, and a facial expression analysis network for health assessment. For multi-part detection network, The YOLOv5s path aggregation network was modified by incorporating a multi-link convolution fusion block (MCFB) to enhance fine-grained feature extraction across objects of different sizes. To improve the detection of dense small targets, a Re-Parameterizable Convolution (RepConv) structure was introduced into the YOLOv5s head. For facial identity recognition, the sixth-stage structure in GhostNet was replaced with a four-layer spatially separable self-attention mechanism (SSSA) to strengthen key feature extraction. Additionally, model compression techniques were applied to optimize the facial expression analysis network for improved efficiency. A transfer learning strategy was employed for weight pre-training, and performance was evaluated using FPS, model weight, mean average precision (mAP), and test set accuracy. Experimental results demonstrated that the enhanced multi-part identification network effectively extracted features from different regions of the sheep flock, achieving an average detection accuracy of 95.84%, with a 2.55% improvement in mAP compared to YOLOv5s. The improved facial classification network achieved a test set accuracy of 98.9%, surpassing GhostNet by 3.1%. Additionally, the facial expression analysis network attained a test set accuracy of 99.2%, representing a 3.6% increase compared to EfficientNet. The proposed system significantly enhances the accuracy and efficiency of sheep flock monitoring by integrating advanced feature extraction and model optimization techniques. The improvements in facial classification and expression analysis further enable real-time health monitoring, contributing to intelligent livestock management.