Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach

2025-10-22

AI Summary

Current image-based waste classification lacks systematic benchmarks for hybrid machine learning (ML) and deep learning (DL) approaches. This paper proposes an efficient hybrid framework: deep features are extracted using ResNet variants and EfficientNetV2-S, then reduced in dimensionality by over 95% via feature selection, before being fed into lightweight discriminative classifiers—namely SVM and logistic regression. This design synergistically leverages the representational power of deep networks and the inference efficiency of classical ML models. Evaluated on TrashNet, an optimized household waste dataset, and the Garbage Classification dataset, the framework achieves 100% accuracy on the first two and 99.87% on the third—surpassing state-of-the-art methods. It also significantly reduces training and inference overhead. The core contribution lies in tightly integrating deep feature extraction with discriminative classification, augmented by interpretable feature selection to achieve a Pareto-optimal trade-off between accuracy and computational efficiency.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Object Detection & Categorization
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Automated image-based garbage classification is a critical component of global waste management; however, systematic benchmarks that integrate Machine Learning (ML), Deep Learning (DL), and efficient hybrid solutions remain underdeveloped. This study provides a comprehensive comparison of three paradigms: (1) machine learning algorithms using handcrafted features, (2) deep learning architectures, including ResNet variants and EfficientNetV2S, and (3) a hybrid approach that utilizes deep models for feature extraction combined with classical classifiers such as Support Vector Machine and Logistic Regression to identify the most effective strategy. Experiments on three public datasets - TrashNet, Garbage Classification, and a refined Household Garbage Dataset (with 43 corrected mislabels)- demonstrate that the hybrid method consistently outperforms the others, achieving up to 100% accuracy on TrashNet and the refined Household set, and 99.87% on Garbage Classification, thereby surpassing state-of-the-art benchmarks. Furthermore, feature selection reduces feature dimensionality by over 95% without compromising accuracy, resulting in faster training and inference. This work establishes more reliable benchmarks for waste classification and introduces an efficient hybrid framework that achieves high accuracy while reducing inference cost, making it suitable for scalable deployment in resource-constrained environments.

Problem

• Developing accurate automated waste image classification systems
• Comparing machine learning, deep learning and hybrid approaches
• Creating efficient models for resource-constrained deployment environments

Innovation

• Hybrid deep learning with classical classifiers
• Feature selection reduces dimensionality by 95%
• Efficient framework for resource-constrained deployment