针对飞鸟识别中存在密集目标识别不准确、小目标检测困难等问题，提出一种基于改进YOLOv8的飞鸟识别算法。首先，针对密集目标识别难度大的问题，采用多尺度线性注意力机制EfficientViT替换骨干网络，实现全局感受野和多尺度学习，提升模型性能和效率的同时提高密集目标识别效果。然后，针对小目标飞鸟检测困难、容易出现漏检的问题，引入高效多尺度注意力（efficient multi-scale attention, EMA）机制，通过通道重组实现跨维度聚合特征，从而更好地捕捉全局信息，实现多尺度特征融合，减少漏检概率。实验结果表明，改进模型在鸟类识别基准数据集CUB-200-2011和自制数据集birds28上的mAP50分别达到77.1%和88.4%，较原始YOLOv8模型分别提高了4.5和5.4个百分点，验证了改进模型的有效性。

In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.