To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
针对传统花卉识别方法分类不准确和泛化能力低等问题,提出一种基于Res Ne Xt50网络和迁移学习的花卉种类识别模型DB-Res Ne Xt50。首先,使用Mixup数据增强方法进行数据扩充;其次,利用预训练Res Ne Xt50模型迁移学习,省去网络训练基础特...针对传统花卉识别方法分类不准确和泛化能力低等问题,提出一种基于Res Ne Xt50网络和迁移学习的花卉种类识别模型DB-Res Ne Xt50。首先,使用Mixup数据增强方法进行数据扩充;其次,利用预训练Res Ne Xt50模型迁移学习,省去网络训练基础特征步骤;最后,在主干网络全连接层前加入含有15层Bottleneck的稠密块,提高特征整合能力。结果表明,DB-Res Ne Xt50网络模型对花卉数据集识别准确率为97.99%。与传统深度学习模型相比,该模型在识别率上有很大提升,总体性能优于现有算法性能。展开更多
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
文摘针对传统花卉识别方法分类不准确和泛化能力低等问题,提出一种基于Res Ne Xt50网络和迁移学习的花卉种类识别模型DB-Res Ne Xt50。首先,使用Mixup数据增强方法进行数据扩充;其次,利用预训练Res Ne Xt50模型迁移学习,省去网络训练基础特征步骤;最后,在主干网络全连接层前加入含有15层Bottleneck的稠密块,提高特征整合能力。结果表明,DB-Res Ne Xt50网络模型对花卉数据集识别准确率为97.99%。与传统深度学习模型相比,该模型在识别率上有很大提升,总体性能优于现有算法性能。
