Application of CNN Classic Model in Modern Image Processing
DOI:
https://doi.org/10.62177/jaet.v1i3.25Keywords:
Convolutional Neural Network, Image Classification, Object Detection, Model Optimization, Deep LearningAbstract
As a deep learning model, convolutional neural network (CNN) has been widely used in the field of modern image processing and has shown excellent performance. From LeNet to AlexNet, to classic models such as VGGNet and ResNet, CNN has achieved remarkable success in tasks such as image classification, object detection and segmentation through multi-level feature extraction and automatic learning capabilities. This paper first explores the basic structure and working principle of CNN, analyzes the advantages and limitations of classic models, and reviews its specific applications in image processing. By introducing the optimization strategies of various models, it further explores the improvement path of CNN in the field of image processing, including model compression, lightweight design and the introduction of new network structures. In short, the continuous optimization of CNN has enabled it to show powerful performance in multiple complex tasks, promoted the rapid development of image processing technology, and provided strong support for applications in more fields.
Downloads
References
Srinija, S.: Image Classification Using Convolutional Neural Networks. International Journal for Research in Applied Science and Engineering Technology, 2022.
Tummala, M.: Image Classification Using Convolutional Neural Networks. International Journal of Scientific and Research Publications (IJSRP), 2019.
Chimakurthi, V. N. S. S.: Application of Convolution Neural Network for Digital Image Processing. Engineering International, 2020.
Rawat, W., Wang, Z.: Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Computation, 2017.
Shen, X., Chen, Y., Tao, X., Jia, J.: Convolutional Neural Pyramid for Image Processing. ArXiv, 2017.
Tian, Y.: Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm. IEEE Access, 2020.
Bao, C., Li, Q., Shen, Z., Tai, C., & Xiang, X.: Approximation Analysis of Convolutional Neural Networks. East Asian Journal on Applied Mathematics. (2023).
Zhang, Q., Chang, J., Meng, G., Xu, S., & Xiang, S.: Learning Graph Structure via Graph Convolutional Networks. Pattern Recognit., 95, 308-318 (2019).
Yamashita, R., Nishio, M., Do, R., & Togashi, K.: Convolutional Neural Networks: An Overview and Application in Radiology. Insights into Imaging, 9(6), 611-629 (2018).
Ayyadevara, V.K.: Convolutional Neural Network. In: Pro Deep Learning with TensorFlow, pp. 179-215 (2018).
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J.: A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33, 6999-7019 (2020).
Gabrielsson, R. B., & Carlsson, G.: A Look at the Topology of Convolutional Neural Networks. ArXiv, abs/1810.03234 (2018).
Zhou, H., & Sun, Q.: Research on Principle and Application of Convolutional Neural Networks. IOP Conference Series: Earth and Environmental Science, 440 (2020).
Mao, T., Shi, Z., & Zhou, D.: Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions. Neural Networks, 144, 778-790 (2021).
Lei, F., Liu, X., Dai, Q., & Ling, B.: Shallow Convolutional Neural Network for Image Classification. SN Applied Sciences, 2 (2019).
Tang, H.: Image Classification based on CNN: Models and Modules. 2022 International Conference on Big Data, Information and Computer Network (BDICN), pp. 693-696 (2022).
Lin, Z., Li, B., Yong, L., Xia, Z., Wang, Y., Wu, J.: FPGA Acceleration of CNNs-Based Malware Traffic Classification. Electronics (2020).
Weerasena, H., Mishra, P.: Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators. ArXiv, abs/2311.00579 (2023).
Ding, X., Xing, L., Lin, T., Wang, J., Li, Y., Miao, Z.: Evaluating CNNs for Military Target Recognition. Lecture Notes in Computer Science, 628-638 (2019).
Khushi, H. M. T., Masood, T., Jaffar, A., Akram, S., Bhatti, S.: Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection. International Journal of Imaging Systems and Technology (2023).
Long, M., Yang, J., Xia, S., Wei, X.: Classification of rotor blade number of rotor targets micro-motion signal based on CNN. 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 182-186 (2021).
Yan, Y.: AlexViT: Novel Diabetic Retinopathy Image Classification. 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 913-918 (2023).
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2024 Zhouyi Wu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
