A Fine-Grained Recognition Method for Cats and Dogs Based on Deep Learning
With the continuous development and expansion of the domestic pet market,fine-grained cat and dog recognition has important research significance for pet management and monitoring.Based on this,this article proposes a fine-grained cat and dog recognition method based on YOLOv8 and improved ShuffleNet-V2.Firstly,the YOLOv8 algorithm's Backbone is used to extract features from cat and dog images,in order to achieve accurate classification of cat and dog breeds and achieve high breed classification accuracy.Afterwards,a ShuffleNet-V2 lightweight twin network combined with ECA attention was proposed,which can accurately capture subtle differences between cat and dog individuals through feature learning,achieving accurate recognition of cat and dog individuals.To verify the effectiveness of the algorithm,a cat and dog breed classification and individual recognition dataset was constructed through public datasets and network collection,and the algorithm was evaluated through multiple quantitative indicators.The experimental results show that the Top1 accuracy of YOLOv8 classification network can reach 88.1%,and the Top5 accuracy can reach 98.6%;After combining the ECA attention mechanism,the twin network achieved accuracies of 90.8%,92.8%,and 92.6%on the training set,validation set,and constructed test pairs,respectively.Finally,the effectiveness of the proposed method was further demonstrated through fine-grained recognition visualization of cats and dogs,combined with a user-friendly UI interface.
deep learningcat and dog recognitionYOLOv8ShuffleNet
NETL
NSTL
万方数据
