摘要： 针对野外复杂背景下濒危动物识别精度不足的问题，该研究对YOLOv8模型进行改进。首先，在主干网络中引进蛇形动态卷积（DSConv），增强模型对遮挡情况下的检测性能；其次，在颈部网络中引入全局注意力模块（GAM），提高模型对濒危动物相关信息的关注度，对环境等不相关的特征进行抑制，减少冗余信息；然后，在头部网络设计小目标检测头，对浅层特征图进行融合，提高网络对小目标的感知和定位能力；最后，使用基于最小点距离的边界框损失回归函数（MPDIoU）替换传统的CIoU算法，从而提高算法的收敛速度和定位精度。实验结果表明，本文模型对于复杂背景下的濒危动物检测精度和平均精确度分别为96.2%和97.2%，相较于基础的YOLOv8n检测精度和平均精确度分别提高了2.1和2.4个百分点。利用相同数据集在不同的目标检测模型上进行对比实验，与Faster-RCNN、SSD、YOLOv5、YOLOv7等模型相比，平均精度均值分别提升了28.7、22.5、3.5、2.4个百分点。实验证明，改进YOLOv8模型可为实现复杂背景下的濒危动物检测提供理论依据。

Abstract: In order to solve the problem of insufficient accuracy in identifying endangered animals in complex backgrounds in the wild, this study improved the YOLOv8 model. First, the Dynamic Snake Convolution (DSConv) was introduced in the backbone network to enhance the detection performance of the model under occlusion. Secondly, the global attention mechanism (GAM) was introduced in the neck network to improved the model's attention to information related to endangered animals, suppress irrelevant features such as the environment, and reduce redundant information. Then, a small target detection head was designed in the head network to fuse shallow feature maps to improved the network's perception and positioning capabilities for small targets. Finally, the bounding box loss regression function based on the minimum point distance (MPDIoU) was used to replace the traditional CIoU algorithm, thereby improving the convergence speed and positioning accuracy of the algorithm. The experimental results show that the detection accuracy and average precision of the proposed model for endangered animals in complex backgrounds are 96.2% and 97.2%, respectively, which are 2.1 and 2.4 percentage points higher than the basic YOLOv8n detection accuracy and average precision, respectively. Using the same data set to conduct comparative experiments on different target detection models, the average precision is increased by 28.7, 22.5, 3.5, and 2.4 percentage points compared with Faster-RCNN, SSD, YOLOv5, YOLOv7 and other models, respectively. The experiment proves that the improved YOLOv8 model can provide a theoretical basis for the detection of endangered animals in complex backgrounds.