Birds play a crucial role in maintaining ecological balance,making bird recognition technology a hot research topic.Traditional recognition methods have not achieved high accuracy in bird identification.This paper pro...Birds play a crucial role in maintaining ecological balance,making bird recognition technology a hot research topic.Traditional recognition methods have not achieved high accuracy in bird identification.This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan.First,a dataset containing five species of local birds in Yunnan was established:C.amherstiae,T.caboti,Syrmaticus humiae,Polyplectron bicalcaratum,and Pucrasia macrolopha.The improved ResNet18 model was then used to identify these species.This method replaces traditional convolution with depth wise separable convolution and introduces an SE(Squeeze and Excitation)module to improve the model’s efficiency and accuracy.Compared to the traditional ResNet18 model,this improved model excels in implementing a wild bird classification solution,significantly reducing computational overhead and accelerating model training using low-power,lightweight hardware.Experimental analysis shows that the improved ResNet18 model achieved an accuracy of 98.57%,compared to 98.26%for the traditional Residual Network 18 layers(ResNet18)model.展开更多
文摘Birds play a crucial role in maintaining ecological balance,making bird recognition technology a hot research topic.Traditional recognition methods have not achieved high accuracy in bird identification.This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan.First,a dataset containing five species of local birds in Yunnan was established:C.amherstiae,T.caboti,Syrmaticus humiae,Polyplectron bicalcaratum,and Pucrasia macrolopha.The improved ResNet18 model was then used to identify these species.This method replaces traditional convolution with depth wise separable convolution and introduces an SE(Squeeze and Excitation)module to improve the model’s efficiency and accuracy.Compared to the traditional ResNet18 model,this improved model excels in implementing a wild bird classification solution,significantly reducing computational overhead and accelerating model training using low-power,lightweight hardware.Experimental analysis shows that the improved ResNet18 model achieved an accuracy of 98.57%,compared to 98.26%for the traditional Residual Network 18 layers(ResNet18)model.
