An Integrated Learning Approach for Municipal Solid Waste Classification

Abstract

The main objective of this study is to develop and evaluate an effective integrated learning approach for the automatic classification of Municipal Solid Waste using the TrashBox dataset, comprising 17,785 images, to improve the sorting of recyclable waste materials, reduce landfill usage, and promote sustainable environmental practices. Initially, four deep learning models-DenseNet161, ResNet152, and MobileNetV3 variants-are explored to determine the most suitable feature extraction method. During the feature selection phase, three metaheuristic algorithms-Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawk Optimization (HHO)-are applied to filter out irrelevant features and retain significant ones. These selected features are then fed into machine learning classifiers-Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbor (KNN)-for final predictions. The DenseNet161-HHO-SVM combination outperforms other models in this study, achieving the highest accuracy and lowest execution time. This integrated approach also demonstrates superior performance (97.45%) compared to previous state-of-The-Art models on the same dataset, with the data processing and method integration phases having substantial impacts.