Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 6th International Conference on Computing and Data Science

Series Vol. 64 , 15 May 2024


Open Access | Article

Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms

Bo Liu * 1 , Liqiang Yu 2 , Chang Che 3 , Qunwei Lin 4 , Hao Hu 5 , Xinyu Zhao 6
1 Zhejiang University
2 The University of Chicago
3 The George Washington University
4 Trine University
5 Zhejiang University
6 Trine University

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 64, 44-50
Published 15 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Bo Liu, Liqiang Yu, Chang Che, Qunwei Lin, Hao Hu, Xinyu Zhao. Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms. ACE (2024) Vol. 64: 44-50. DOI: 10.54254/2755-2721/64/20241374.

Abstract

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.

Keywords

Deep Learning, Computer Vision, Image Classification, Object Detection, End-to-End, Performance Analysis

References

1. Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.

2. Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023.

3. Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.Ncevarlk A , Akn A , Adali E .The Effect of Filter Usage on the Results in Image Classification with Artificial Intelligence/Deep Learning Methods[J].2022 7th International Conference on Computer Science and Engineering (UBMK), 2022:450-455.

4. Mogadala A , Kalimuthu M , Klakow D .Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods[J].Journal of Artificial Intelligence Research, 2021, 71:1183-1317.

5. Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.

6. Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023.

7. Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.

8. Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.

9. Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 6th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-425-5
ISBN (Online)
978-1-83558-426-2
Published Date
15 May 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/64/20241374
Copyright
15 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated