基于注意力网络的长时牦牛个体识别研究

Research on long-term yak individual recognition based on attention networks

为推动精准畜牧业的发展及探讨长时间跨度下的动物个体识别,构建间隔6个月和12个月的同一批牦牛个体图像数据集。试验采用引入注意力机制的PCB+SE-ResNet50识别模型,实现短时和长时牦牛个体识别,从而分析影响长时牦牛个体识别的因素,并在该长时数据集上与ViT和PGCFL模型识别结果进行比较。结果表明:该模型在间隔6个月和12个月的数据集上识别平均精度均值达到60.37%、41.56%。相较于ViT,分别提高1.64%、5.82%;相较于PGCFL,分别提高12.40%、11.22%。该研究可为长时牦牛个体识别、养殖信息化及牲畜精准管理等提供理论依据和方法指导。

In order to promote the development of precision animal husbandry and discuss the long-term span of animal individual recognition,in this paper,the same batch of yak individual image datasets with an interval of 6 months and 12 months are constructed.In the experiment,the PCB+SE-ResNet50 recognition model with attention mechanism was used to realize short-term and long-term yak individual recognition,so as to analyze the factors affecting long-term yak individual recognition.The recognition results of this long-term dataset were compared with those of ViT and PGCFL models.The results showed that the mean average precision of the model reached 60.37%and 41.56%on the data set with an interval of 6 months and 12 months.Compared with ViT,it was 1.64%and 5.82%higher,respectively,and compared with PGCFL,it was 12.40%and 11.22%higher,respectively.This study can provide theoretical basis and method guidance for long-term yak individual identification,breeding information and precision management of livestock.