Classification of waste using region-based convolutional neural network

Abstract

The rapid increase in municipal solid waste poses significant environmental and public health challenges, particularly in rapidly urbanizing regions such as Kampala, Uganda. Traditional manual classification methods remain inefficient, error-prone, and costly, leading to low recycling rates and over-reliance on landfills. This study addresses these challenges by developing and evaluating a machine learning framework for automated waste classification. A secondary dataset of waste images was preprocessed and subjected to a two-phase training pipeline, integrating convolutional feature extraction with optimized classifiers. To ensure fairness and robustness, the study implemented bias mitigation techniques such as Synthetic Minority Oversampling (SMOTE) and applied hyperparameter optimization to improve model generalization. Benchmark comparisons with alternative architectures (VGG16, ResNet50, EfficientNet-B0) and classifiers (Support Vector Machines, Random Forests, Neural Networks) were conducted. Results demonstrate that the optimized Support Vector Machine achieved the best classification accuracy at 99.55%, outperforming other models across accuracy, F1-score, and real-world validation. The system was deployed through an interactive Streamlit interface, providing real-time prediction, visual performance analysis, and production-ready usability. These findings confirm the viability of machine learning– driven waste classification in resource-constrained contexts, offering a scalable solution to improve recycling efficiency, reduce operational costs, and support Uganda‘s transition toward sustainable waste management practices.