Analysis of convolutional neural networks and its application in object detection

Research Article
Open access

Analysis of convolutional neural networks and its application in object detection

Yiming Gao 1*
  • 1 Vincent Massey Secondary School    
  • *corresponding author Quintonice0@gmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/14/20230759
ACE Vol.14
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-019-6
ISBN (Online): 978-1-83558-020-2

Abstract

With the development of times and technology, artificial intelligence-based neural network algorithms have been widely used in scientific research and life. Among them, Convolutional neural network (CNN) is the most classic and the most representative. CNN have been widely used in images classification in recent years. This article focuses on the basic information of the convolutional neural networks and its applications especially in object detection. Also, the advantages of the CNN and the possible future improvements will be told in this article. In the end of this article some research of experiments will be shown in order to prove the accuracy of convolutional neural networks in detecting objects and visualizing results. This article gathers basic knowledge of convolutional neural networks. As a result, it may help new starters to get to know the CNN and then make contributions to the development of deep learning especially the CNN according to the advantages and future improvements mentioned in the article.

Keywords:

CNN, application, object detection

Gao,Y. (2023). Analysis of convolutional neural networks and its application in object detection. Applied and Computational Engineering,14,63-67.
Export citation

References

[1]. T. Li, Y. Yin, Z. Yi, Z. Guo, Z. Guo, and S. Chen, “Evaluation of a convolutional neural network to identify scaphoid fractures on radiographs,” Journal of Hand Surgery (European Volume), p. 175319342211270, Oct. 2022.

[2]. M. Mandal, “CNN for Deep Learning | Convolutional Neural Networks (CNN),” Analytics Vidhya, May 01, 2021. https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-cnn/

[3]. E. Aldhahri et al., “Correction to: Arabic Sign Language Recognition Using Convolutional Neural Network and MobileNet,” Arabian Journal for Science and Engineering, vol. 48, no. 2, pp. 2615–2615, Sep. 2022.

[4]. M. Liu et al., “FocusedDropout for Convolutional Neural Network,” Applied Sciences, vol. 12, no. 15, p. 7682, Jul. 2022.

[5]. J. Sikora, R. Wagnerová, L. Landryová, J. Šíma, and S. Wrona, “Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network,” Applied Sciences, vol. 11, no. 16, p. 7484, Aug. 2021.

[6]. M. Walia, “Object Detection, Image Classification, Keypoint Detection,” Roboflow Blog, Sep. 28, 2022. https://blog.roboflow.com/object-detection-vs-image-classification-vs-keypoint-detection/

[7]. Y. Li, M. Lei, Y. Cheng, R. Wang, and M. Xu, “Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis,” Science Progress, vol. 105, no. 4, p. 003685042211354, Oct. 2022.

[8]. M. H. Ghaffari et al., “Deep convolutional neural networks for the detection of diarrhea and respiratory disease in preweaning dairy calves using data from automated milk feeders,” Journal of Dairy Science, vol. 105, no. 12, pp. 9882–9895, Dec. 2022.

[9]. M. I. Khairul Islam, R. I. Meem, F. B. Abul Kasem, A. Rakshit, and Md. T. Habib, “Bangla Spell Checking and Correction Using Edit Distance,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1-4, 2019.

[10]. G.-S. Liu, P.-Y. Huang, M.-L. Wen, S.-S. Zhuang, J. Hua, and X.-P. He, “Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network,” World Journal of Gastroenterology, vol. 28, no. 22, pp. 2457–2467, Jun. 2022.

Cite this article

Gao,Y. (2023). Analysis of convolutional neural networks and its application in object detection. Applied and Computational Engineering,14,63-67.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-019-6(Print) / 978-1-83558-020-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.14
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. T. Li, Y. Yin, Z. Yi, Z. Guo, Z. Guo, and S. Chen, “Evaluation of a convolutional neural network to identify scaphoid fractures on radiographs,” Journal of Hand Surgery (European Volume), p. 175319342211270, Oct. 2022.

[2]. M. Mandal, “CNN for Deep Learning | Convolutional Neural Networks (CNN),” Analytics Vidhya, May 01, 2021. https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-cnn/

[3]. E. Aldhahri et al., “Correction to: Arabic Sign Language Recognition Using Convolutional Neural Network and MobileNet,” Arabian Journal for Science and Engineering, vol. 48, no. 2, pp. 2615–2615, Sep. 2022.

[4]. M. Liu et al., “FocusedDropout for Convolutional Neural Network,” Applied Sciences, vol. 12, no. 15, p. 7682, Jul. 2022.

[5]. J. Sikora, R. Wagnerová, L. Landryová, J. Šíma, and S. Wrona, “Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network,” Applied Sciences, vol. 11, no. 16, p. 7484, Aug. 2021.

[6]. M. Walia, “Object Detection, Image Classification, Keypoint Detection,” Roboflow Blog, Sep. 28, 2022. https://blog.roboflow.com/object-detection-vs-image-classification-vs-keypoint-detection/

[7]. Y. Li, M. Lei, Y. Cheng, R. Wang, and M. Xu, “Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis,” Science Progress, vol. 105, no. 4, p. 003685042211354, Oct. 2022.

[8]. M. H. Ghaffari et al., “Deep convolutional neural networks for the detection of diarrhea and respiratory disease in preweaning dairy calves using data from automated milk feeders,” Journal of Dairy Science, vol. 105, no. 12, pp. 9882–9895, Dec. 2022.

[9]. M. I. Khairul Islam, R. I. Meem, F. B. Abul Kasem, A. Rakshit, and Md. T. Habib, “Bangla Spell Checking and Correction Using Edit Distance,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1-4, 2019.

[10]. G.-S. Liu, P.-Y. Huang, M.-L. Wen, S.-S. Zhuang, J. Hua, and X.-P. He, “Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network,” World Journal of Gastroenterology, vol. 28, no. 22, pp. 2457–2467, Jun. 2022.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.