2026 年 19 巻 2 号 p. 105-110
An intelligent monitoring model based on YOLOv5 and an attention mechanism is proposed to improve automatic identification of abnormal pig behaviors in farming environments. A dataset containing pig fighting, abnormal feeding, and reduced activity behaviors was constructed and enhanced through data augmentation. By integrating a channel attention mechanism into YOLOv5s, the model improves feature extraction and detection performance for small and occluded targets. Experimental results show that the improved model achieves 95.24 % detection accuracy and 91 FPS, outperforming Faster R-CNN, YOLOv3, and the original YOLOv5s. The model demonstrates strong robustness, stability, and computational efficiency under complex conditions, providing effective technical support for intelligent livestock health management and precision farming applications.
