基于困难样本挖掘的多尺度细粒度对象识别方法OA

To solve the issues of breed classification and individual identification in fine-grained cat and dog identification tasks,a lightweight visual model PetXNet was proposed.This model combined the YOLOv8 backbone network with a feature pyramid network(FPN)to achieve efficient feature extraction and multi-scale feature fusion.A triplet loss and hard sample mining strategy were adopted to improve the discriminative capability for different individuals.A phased training strategy was proposed to address different granularities of the dataset,and progressed from breed classification to individual identification through a coarse-to-fine and fine-to-coarse training approach.Based on public datasets,a self-constructed dataset was developed to fill the gap in individual verification data for pet biometrics.Experimental results show that PetXNet achieves high accuracy and generalization on both public and self-constructed datasets,and the model achieves 92.7％accuracy in breed classification tasks and 93.6％accuracy in individual verification tasks,verifying its effectiveness in fine-grained recognition tasks.