Research on Lightweight Models for Detection of Large Herbivores in the Yellow River Source Region Based on UAV Images

In response to the issues of insufficient lightweight models and poor real-time monitoring and detection performances of UAV images in detecting large herbivore, this study aimed to develop a lightweight model to improve on the YOLOv5 model to ensure detection accuracy. Large herbivores in Maduo County, the source area of the Yellow River, were used as detection materials in this study. Results showed that the improved algorithm significantly increased the degree of lightweightness and maintained the basic accuracy. The model achieved a mean average precision mAP@0.5:0.95 of 0.51 and mAP@0.5 of 0.96, surpassing those of the original model. The parameter count, computation, and model size were reduced by 91.56, 89.87, and 88.19%, respectively. Compared to mainstream YOLOv5 and other object detection models, the improved model in this study showed reductions in computational complexity and parameter count, making it suitable for lightweight application on low-power platforms and mobile devices. The lightweight models and tools provided in this study can be applied to grassland herbivore surveys and grassland carrying capacity assessments, providing technical support for grassland management and livestock industry planning.