现代电子技术2026，Vol.49Issue(19)：31-36,6.DOI:10.16652/j.issn.1004-373X.2026.19.005
基于长短期记忆网络与MobileNet V2的图像分类研究
Research on image classification based on long and short-term memory network and MobileNet V2
摘要
Abstract
An image classification framework based on MobileNet V2 and long and short-term memory(LSTM)network is proposed,which aims to obtain more efficient and accurate image classification results.MobileNet V2 is used to classify images,and the LSTM network is used to enhance the performance of the model by maintaining the feature state information encountered in the previous process of image classification.The attention mechanism can be easily integrated into the standard convolutional neural network(CNN)architecture to improve the model accuracy and prediction accuracy at the expense of the minimum computing load.Therefore,this attention mechanism is adopted to improve the whole MobileNet V2 model to achieve the desired effect.The CIFAR-10 and CIFAR-100 datasets are used for classification tasks in order to verify the performance of the model.Experiments show that the proposed model effectively reduces the computational complexity and required parameters compared with other CNN architectures on CIFAR-10 and CIFAR-100 datasets.To sum up,the proposed model can promote the application and development of related technologies in multiple fields.关键词
图像分类/MobileNet V2/长短期记忆网络/注意力机制/卷积神经网络/损失函数Key words
image classification/MobileNet V2/LSTM network/attention mechanism/CNN/loss function分类
信息技术与安全科学引用本文复制引用
李航,陈思行,殷守林,滕琳,王加驰..基于长短期记忆网络与MobileNet V2的图像分类研究[J].现代电子技术,2026,49(19):31-36,6.基金项目
辽宁省科技联合计划项目(2024JH2/102600106) （2024JH2/102600106）
辽宁省教育厅项目(LJ232510166008) （LJ232510166008）
