基于卷积神经网络的图像分类模型综述

Review of image classification models based on convolutional neural networks

卷积神经网络在计算机视觉等领域占有一席之地,利用局部连接、权值共享以及池化操作等特性,有效地提取图像的局部特征,降低网络复杂度,具有更少的参数量和更好的鲁棒性,因此,吸引了众多研究者的关注,使分类模型朝着更轻、更快、更高效的方向迅速发展。按照卷积神经网络发展的时间线,介绍了常用的典型网络模型,剖析了其创新点与优缺点,并对其未来的发展方向进行了展望。

Convolutional neural networks have established themselves as a prominent technique in computer vision and related fields.By leveraging features such as local connections,weight sharing,and pooling operations,these networks are able to effectively extract local features from images,reducing network complexity,and exhibiting fewer parameters and greater robustness.As a result,they have garnered significant attention from researchers and have led to the rapid development of classification models that are lighter,faster,and more efficient.This article presents a timeline of typical network models used in convolutional neural network development,analyzes their innovative points and advantages and disadvantages,and offers insights into their future development directions.