Comparative Study on Binary Waste Classification Based on Deep Convolutional Neural Networks and Data Augmentation

Shuyuan Xing

2025

Abstract

With the acceleration of urbanization and the increasing demand for environmental protection, waste classification has emerged as a crucial component of waste management. This paper proposes three baseline methods for binary waste classification based on deep convolutional neural networks and data augmentation techniques. The first baseline employs a pre-trained ResNet50 model combined with an SE attention module to enhance feature representation; the second baseline utilizes a lightweight EfficientNet-B0 model with conventional data augmentation strategies; and the third baseline also adopts EfficientNet-B0 but integrates more aggressive augmentation methods, such as random cropping, color jittering, Gaussian blur, and random erasing, to improve model generalization. Results from experiments on a Kaggle trash categorization dataset show that the EfficientNet-B0-based method with aggressive data augmentation significantly increases accuracy and robustness. This paper serves as a helpful reference for further research in this area since it not only presents an efficient deep learning solution for waste classification, but it also provides insightful information about how data augmentation techniques affect model performance.

Paper Citation

in Harvard Style

Xing S. (2025). Comparative Study on Binary Waste Classification Based on Deep Convolutional Neural Networks and Data Augmentation. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 404-408. DOI: 10.5220/0013698300004670

in Bibtex Style

@conference{icdse25,
author={Shuyuan Xing},
title={Comparative Study on Binary Waste Classification Based on Deep Convolutional Neural Networks and Data Augmentation},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={404-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013698300004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Comparative Study on Binary Waste Classification Based on Deep Convolutional Neural Networks and Data Augmentation
SN - 978-989-758-765-8
AU - Xing S.
PY - 2025
SP - 404
EP - 408
DO - 10.5220/0013698300004670
PB - SciTePress