Individual Identification Method of Leopard in Multiple Scenarios
Runping Xi, Sisi wang, Yue Liu, Zhao Wang · 2021
Individual identification of rare animals in different scenarios helps to accurately grasp the number and scale of animal populations, so as to formulate appropriate protection measures and policies, which are beneficial to maintaining human ecological balance and sustainable development. In this paper, we study the individual identification methods of leopard in multiple scenes, obtain the leopard image data through various channels such as zoos and protected areas, and construct a dataset, which obtains 28,751 images of leopard. In order to extract more fine-grained features of the leopard, Res2net was added to the network, at the same time, the self-attention mechanism was added to improve the global correlation. A R2NMGN method based on the self-attention mechanism was proposed. The experimental results show that the method proposed in this paper is significantly better than other methods in individual recognition of leopard.