ILCNN: An Improved Lightweight Convolutional Neural Network Based Recycling Garbage Classification Strategy with Image Processing Technique

S. G. Balakrishnan, S. Abinaya, M. Harinishree, M. Jansitha, S. Kalaivani

2025

Abstract

Domestic garbage has grown at an alarming rate in recent years, making the use of intelligent waste sorting technology an absolute necessity. Unfortunately, embedded garbage classification devices aren't a good fit for current garbage classification algorithms due to their high parameter counts and bad real-time performance. More and more people are using traditional garbage cans, which mean there's a growing need for effective segmentation and identification algorithms. Modern computer systems' increased processing power and more effective picture recognition technologies are in line with this desire. By utilizing an image processing logic known as Improved Lightweight Convolutional Neural Network (ILCNN), a new garbage classification procedure is established, which decreases the time and expenses associated with waste segregation. This helps to solve these challenges against test how well the suggested model works, it is compared against a standard deep learning model known as a Convolutional Neural Network (CNN). Reducing the need for human involvement and increasing efficiency in garbage segregation are the goals of automating the process. Using a publicly accessible dataset that included pictures of different kinds of garbage gathered from different places, we ran the various state-of-the-art deep learning models. On this dataset, we fine-tuned pre-trained ILCNN models using image augmentation approaches and transfer learning techniques. With its proposed ILCNN model, the network can classify and recognize garbage with great accuracy while using very little energy.

Paper Citation

in Harvard Style

Balakrishnan S., Abinaya S., Harinishree M., Jansitha M. and Kalaivani S. (2025). ILCNN: An Improved Lightweight Convolutional Neural Network Based Recycling Garbage Classification Strategy with Image Processing Technique. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 59-69. DOI: 10.5220/0013876900004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Balakrishnan and S. Abinaya and M. Harinishree and M. Jansitha and S. Kalaivani},
title={ILCNN: An Improved Lightweight Convolutional Neural Network Based Recycling Garbage Classification Strategy with Image Processing Technique},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={59-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013876900004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - ILCNN: An Improved Lightweight Convolutional Neural Network Based Recycling Garbage Classification Strategy with Image Processing Technique
SN - 978-989-758-777-1
AU - Balakrishnan S.
AU - Abinaya S.
AU - Harinishree M.
AU - Jansitha M.
AU - Kalaivani S.
PY - 2025
SP - 59
EP - 69
DO - 10.5220/0013876900004919
PB - SciTePress