Research on long-term yak individual recognition based on attention networks
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.
万方数据
维普
