The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130757
PDF

Improved Spatial Invariance for Vehicle Platoon Application using New Pooling Method in Convolution Neural Network

Author 1: M S Sunitha Patel
Author 2: Srinath S

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The imbalanced dataset is a prominent concern for automotive deep learning researchers. The proposed work provides a new mixed pooling strategy with enhanced performance for imbalanced vehicle dataset based on Convolution Neural Network (CNN). Pooling is crucial for improving spatial invariance, processing time, and overfitting in CNN architecture. Max and average pooling are often utilized in contemporary research articles. Both techniques of pooling have their own advantages and disadvantages. In this study, the advantages of both pooling algorithms are evaluated for the classification of three vehicles: car, bus, and truck for imbalanced datasets. For each epoch, the performance of max pooling, average pooling, and the new mixed pooling method was assessed using ROC, F1-score, and error rate. Comparing the performance of the max-pooling method to that of the average pooling method, it has been found that the max-pooling method is superior. The performance of the proposed mixed pooling approach is superior to that of the maximum pooling and average pooling methods. In terms of Receiver Operating Characteristics (ROC), the proposed mixed pooling technique is approximately 2 per cent better than the maximum pooling method and 8 per cent better than the mixed pooling method. Using a new pooling technique, the classification performance with an imbalanced dataset is improved, and also a novel mixed pooling method is proposed for the classification of vehicles.

Keywords: Average pooling; convolution neural network; imbalance dataset; max pooling; mixed pooling

M S Sunitha Patel and Srinath S, “Improved Spatial Invariance for Vehicle Platoon Application using New Pooling Method in Convolution Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130757

@article{Patel2022,
title = {Improved Spatial Invariance for Vehicle Platoon Application using New Pooling Method in Convolution Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130757},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130757},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {M S Sunitha Patel and Srinath S}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org