A Sheep Behavior Recognition Approach Based on Improved FESS-YOLOv8n Neural Network.

Abstract

Sheep are an important breed of livestock in the northern regions of China, providing humans with nutritious meat and by-products. Therefore, it is essential to ensure the health status of sheep. Research has shown that the individual and group behaviors of sheep can reflect their overall health status. However, as the scale of farming expands, traditional behavior detection methods based on manual observation and those that employ contact-based devices face challenges, including poor real-time performance and unstable accuracy, making them difficult to meet the current demands. To address these issues, this paper proposes a sheep behavior detection model, Fess-YOLOv8n, based on an enhanced YOLOv8n neural network. On the one hand, this approach achieves a lightweight model by introducing the FasterNet structure and the selective channel down-sampling module (SCDown). On the other hand, it utilizes the efficient multi-scale attention mechanism (EMA)as well as the spatial and channel synergistic attention module (SCSA) to improve recognition performance. The results on a self-built dataset show that Fess-YOLOv8n reduced the model size by 2.56 MB and increased the detection accuracy by 4.7%. It provides technical support for large-scale sheep behavior detection and lays a foundation for sheep health monitoring.