MSFResNet:A ResNeXt50 model based on multi-scale feature fusion for wild mushroom identification  

MSFResNet:面向野生菌识别的多尺度特征融合的ResNeXt50模型

作　　者：YANG Yang JU Tao YANG Wenjie ZHAO Yuyang 杨阳;巨涛;杨文杰;赵宇阳(兰州交通大学电子与信息工程学院,甘肃兰州730070)

机构地区：[1]School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730000,China

出　　处：《Journal of Measurement Science and Instrumentation》2025年第1期66-74,共9页测试科学与仪器(英文版)

基　　金：supported by National Natural Science Foundation of China(No.61862037);Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。

摘　　要：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.针对细粒度图像特征信息冗余、特征表征差异不明显、识别准确率较低的问题,在Res Ne Xt50模型的基础上,提出了一种融合多尺度特征信息的网络模型MSFRes Net。首先,设计了一个多尺度特征提取模块,利用不同尺度的卷积核获取特征图的多尺度信息,并利用通道注意力机制增加网络对全局信息的获取。其次,通过短连接将多尺度特征提取模块处理后的特征图与深层特征图融合,以引导网络充分学习,改善深层网络特征图纹理细节丢失的问题,以提升网络泛化能力和识别准确率。最后,使用公共数据集验证了MSFRes Net模型的有效性,并将该模型应用于野生菌识别。实验结果表明,在公共数据集FGVC-Aircraft上,MSFRes Net模型相较于Res Ne Xt50模型,准确率提升了6.01%;在野生菌数据集上,MSFRes Net模型的分类准确度为99.13%,比Res Ne Xt50模型提升了0.47%。热力图实验结果表明,MSFRes Net模型明显地减少了背景信息的干扰,使网络重点关注野生菌主体所在位置,可有效提升野生菌识别的准确率。

关 键 词：multi-scale feature fusion attention mechanism ResNeXt50 wild mushroom identification deep learning 

分 类 号：G63[文化科学—教育学]