Review of Deep Learning Models for Image Classification Based on Convolutional Neural Networks

被引：0

作者：

Liu, Hongda ^{[1
]}

Sun, Xuhui ^{[1
]}

Li, Yibin ^{[1
]}

Han, Lin ^{[1
]}

Zhang, Yu ^{[1
]}

机构：

[1] Institute of Marine Science and Technology, Shandong University, Shandong, Qingdao

来源：

Computer Engineering and Applications | 2025年 / 61卷 / 11期

关键词：

convolutional neural networks; deep learning; image classification; Transformer;

D O I：

10.3778/j.issn.1002-8331.2411-0196

中图分类号：

学科分类号：

摘要：

Using neural network model for classification has always been a very important research direction. With the development of deep learning technology, the requirement for neural network model is getting higher and higher. At the same time, high recognition rate, the number of parameters and training time of the model are also highly required. Convolutional neural networks have always been the mainstream method for image classification in deep learning. This paper mainly introduces the development history of convolutional neural networks for classification model, and analyzes the construction ideas of each model at different stages. Secondly, the paper reviews relevant examples of Transformer combined with convolutional neural networks as well as the application of each model in other fields. Finally, the possible development directions of convolutional neural networks are discussed. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：1 / 21

页数：20

共 134 条

[51]

LIU Q H, LIU W K, LIU Y S, Et al., Rice grains and grain impurity segmentation method based on a deep learning algorithm-NAM-EfficientNetV2, Computers and Electronics in Agriculture, 209, (2023)

[52]

DENG W J, MARSH J, GOULD S, Et al., Fine-grained classification via categorical memory networks, IEEE Transactions on Image Processing, 31, pp. 4186-4196, (2022)

[53]

KANG S Y, ZHANG Q L, WEI H R, Et al., An efficient multiscale integrated attention method combined with hyperspectral system to identify the quality of rice with different storage periods and humidity, Computers and Electronics in Agriculture, 213, (2023)

[54]

YULDASHEV Y, MUKHIDDINOV M, ABDUSALOMOV A B, Et al., Parking lot occupancy detection with improved MobileNetV3, Sensors, 23, 17, (2023)

[55]

FAN S J, LIANG W, DING D R, Et al., LACN: a lightweight attention- guided ConvNext network for low- light image enhancement, Engineering Applications of Artificial Intelligence, 117, (2023)

[56]

DONG X, LI D, FANG J D., FCCD-SAR: a lightweight SAR ATR algorithm based on FasterNet, Sensors, 23, 15, (2023)

[57]

MAO Y Y, ZHANG N H, WANG Q, Et al., Multi-level dispersion residual network for efficient image super-resolution, Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1660-1669, (2023)

[58]

QI J D, WANGDUI B B, JIANG J, Et al., EDKSANet: an efficient dual- kernel split attention neural network for the classification of Tibetan medicinal materials, Electronics, 12, 20, (2023)

[59]

PARK S, JEONG Y, CHOI Y S., Efficient dual attention transformer for image super- resolution, Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, pp. 963-970, (2024)

[60]

NGO T T, HUH E N, HONG C S., ETANet: an efficient triple-attention network for salient object detection, Proceedings of the 2023 International Conference on Information Networking, pp. 271-276, (2023)

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