Research on Optimization of Image Recognition Algorithm Based on Deep Learning

Mingyuan Zhang

doi:10.62051/ijcsit.v4n1.26

Authors

Mingyuan Zhang

DOI:

https://doi.org/10.62051/ijcsit.v4n1.26

Keywords:

Deep learning, Image recognition, Algorithm optimization, Convolutional neural networks

Abstract

With the rapid development of information technology, image recognition has become increasingly crucial in numerous fields. Deep learning, as a highly potent machine learning approach, has brought about a revolutionary breakthrough in image recognition. This paper delves into the optimization of image recognition algorithms based on deep learning. Firstly, it analyzes the application status of deep learning in image recognition, covering common neural network architectures such as convolutional neural network (CNN). Then, the optimization strategy of the image recognition algorithm is elaborated from multiple aspects, including data augmentation to increase data diversity, model structure optimization for better feature extraction, and hyperparameter adjustment to enhance performance. Through extensive experiments, the effects of different optimization methods are compared meticulously. It is demonstrated that the optimized algorithm shows significant improvements in accuracy, robustness, and efficiency. Finally, the paper looks ahead to the future development trend of image recognition algorithms based on deep learning, envisioning further advancements and broader applications.

Downloads

Download data is not yet available.

References

[1] Zhong, Y., Liu, Y., Gao, E., Wei, C., Wang, Z., & Yan, C. (2024). Deep Learning Solutions for Pneumonia Detection: Performance Comparison of Custom and Transfer Learning Models. medRxiv, 2024-06.

[2] Gu, W., Zhong, Y., Li, S., Wei, C., Dong, L., Wang, Z., & Yan, C. (2024). Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis. arXiv preprint arXiv:2407.16150.

[3] Liu, J., Li, K., Zhu, A., Hong, B., Zhao, P., Dai, S., ... & Su, H. (2024). Application of Deep Learning-Based Natural Language Processing in Multilingual Sentiment Analysis. Mediterranean Journal of Basic and Applied Sciences (MJBAS), 8(2), 243-260.

[4] Xu, Q., Feng, Z., Gong, C., Wu, X., Zhao, H., Ye, Z., ... & Wei, C. (2024). Applications of Explainable AI in Natural Language Processing. Global Academic Frontiers, 2(3), 51-64.

[5] Gao, H., Wang, H., Feng, Z., Fu, M., Ma, C., Pan, H., ... & Li, N. (2016). A novel texture extraction method for the sedimentary structures’ classification of petroleum imaging logging. In Pattern Recognition: 7th Chinese Conference, CCPR 2016, Chengdu, China, November 5-7, 2016, Proceedings, Part II 7 (pp. 161-172). Springer Singapore.

[6] Li, W., Li, H., Gong, A., Ou, Y., & Li, M. (2018, August). An intelligent electronic lock for remote-control system based on the internet of things. In journal of physics: conference series (Vol. 1069, No. 1, p. 012134). IOP Publishing.

[7] Wang, Z., Yan, H., Wei, C., Wang, J., Bo, S., & Xiao, M. (2024). Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning. arXiv preprint arXiv:2408.03084.

[8] Bo, S., Zhang, Y., Huang, J., Liu, S., Chen, Z., & Li, Z. (2024). Attention Mechanism and Context Modeling System for Text Mining Machine Translation. arXiv preprint arXiv:2408.04216.

[9] Zhou, R. (2024). Understanding the Impact of TikTok's Recommendation Algorithm on User Engagement. International Journal of Computer Science and Information Technology, 3(2), 201-208.

[10] Zhou, R. (2024). Advanced Embedding Techniques in Multimodal Retrieval Augmented Generation a Comprehensive Study on Cross Modal AI Applications. Journal of Computing and Electronic Information Management, 13(3), 16-22.

[11] Zhang, Y., & Fan, Z. (2024). Memory and Attention in Deep Learning. Academic Journal of Science and Technology, 10(2), 109-113.

[12] Zhang, Y., & Fan, Z. (2024). Research on Zero knowledge with machine learning. Journal of Computing and Electronic Information Management, 12(2), 105-108.

[13] Wang, J., Zhang, H., Zhong, Y., Liang, Y., Ji, R., & Cang, Y. (2024). Advanced Multimodal Deep Learning Architecture for Image-Text Matching. arXiv preprint arXiv:2406.15306.

[14] Wang, J., Li, X., Jin, Y., Zhong, Y., Zhang, K., & Zhou, C. (2024). Research on image recognition technology based on multimodal deep learning. arXiv preprint arXiv:2405.03091.

[15] Yao, Y. (2024, May). Design of Neural Network-Based Smart City Security Monitoring System. In Proceedings of the 2024 International Conference on Computer and Multimedia Technology (pp. 275-279).

[16] Xu, T. (2024). Comparative Analysis of Machine Learning Algorithms for Consumer Credit Risk Assessment. Transactions on Computer Science and Intelligent Systems Research, 4, 60-67.

[17] Xu, T. (2024). Credit Risk Assessment Using a Combined Approach of Supervised and Unsupervised Learning. Journal of Computational Methods in Engineering Applications, 1-12.

[18] Xia, Y., Liu, S., Yu, Q., Deng, L., Zhang, Y., Su, H., & Zheng, K. (2023). Parameterized Decision-making with Multi-modal Perception for Autonomous Driving. arXiv preprint arXiv:2312.11935.

[19] Qiu, L., & Liu, M. (2024). Innovative Design of Cultural Souvenirs Based on Deep Learning and CAD.

[20] Liu, M., & Li, Y. (2023, October). Numerical analysis and calculation of urban landscape spatial pattern. In 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023) (pp. 113-119). Atlantis Press.