A livestock recognition and detection algorithm based on improved YOLOv5s

As the largest natural reserve in China, the Three-River-Source Region possesses abundant grassland resources. However, in recent years, the grasslands in this area have been facing severe degradation issues. Overgrazing by livestock, mainly cattle and sheep, who are the primary livestock of the local herdsmen, has led to excessive grassland depletion. Addressing this phenomenon, this paper aims to provide technical support for grassland workers to explore the relationship between livestock grazing and grassland degradation by accurately identifying the number of cattle and sheep. This will enable the formulation of more effective grazing management strategies. This study involved field data collection and manual annotation to construct a dataset comprising 10,000 images of cattle and sheep. Building upon the original YOLOv5 model, optimizations were made by integrating compression techniques and the Squeeze-and-Excitation (SE) module, as well as enhancing the small object detection stage. Following these optimizations, the model achieved performance improvements of 1.2% in precision, 4.03% in recall, and 3.6% in mAP0.5 (mean average precision at an IoU threshold of 0.5) compared to the original model. These enhancements provide efficient and accurate technical means for livestock counting in the Three-River-Source Region.