Abstract: Convolutional neural networks have achieved many results in computer vision tasks,both in target detection and segmentation,which depend on the extracted feature information.Some problems such as ambiguous data and varying object shapes pose great challenges for feature extraction.The traditional convolutional structure can only learn the contextual information of the neighboring spatial locations of the feature map and cannot extract the global information,while models such as the self-attentive mechanism,although having a larger perceptual field and establishing global dependencies,are insufficient due to their high computational complexity and the need for large amounts of data.Therefore,this paper proposes a model combining CNN and LSTM,which can better combine the global information of image data while enhancing the local perceptual field.It uses the backbone network ConvNeXt-T as the base model to solve the problem of different object shapes by splicing different size convolutional kernels to fuse multi-scale features,and aggregates two-way long and short-term memory networks from both horizontal and vertical directions.Focus on the interactivity of global and local information.Experiments are conducted on publicly accessible CIFAR-10,CIFAR-100,and Tiny ImageNet datasets for image classification tasks,and the accuracy of the proposed network improves 3.18%,2.91%,and 1.03% in the three datasets respectively,compared to the base model ConvNeXt-T.Experiments demonstrate that the improved ConvNeXt-T network has substantially improved the number of parameters and accuracy compared with the base model,and can extract more effective feature information.

Key words: Feature extraction, Local receptive field, ConvNeXt-T, Multi-scale features, Bidirectional long and short-term memory network