DESA-YOLO: A Growth-Stage Adaptive Pig Face Recognition Algorithm Based on Multi-Scale Feature Fusion

This paper proposes a pig face individual recognition algorithm named DESA-YOLO based on an improved YOLO11 model, aiming to address the adaptability issue of pig face recognition across different growth stages. With the large-scale development of pig farming, traditional individual identification methods suffer from low efficiency and high cost, while pig face recognition technology has great application potential as an important tool for precision suckling and disease prevention. Due to the significant facial feature differences among pigs at different growth stages, this study proposes an improved YOLO11 architecture to address this challenge. The method improves detection accuracy and adaptability by introducing a DualConv structure, an EMA module, a SEAM attention mechanism, and an ASFF detection head. Experimental results show that DESA-YOLO achieves significant improvements over traditional models such as YOLOv5 and YOLOv8 in precision, recall, mAP, and F1 score, obtaining an mAP of 93.7%, which represents increases of 6.3%, 3.5%, and 3% in precision, recall, and mAP respectively compared with the YOLO11 baseline model. Ablation experiments and heatmap visualizations further validate the effectiveness of the proposed improvement modules. The improved model demonstrates higher adaptability and stability across different pig growth stages, while maintaining real-time inference performance for practical deployment.