Optimizing Solid Waste Classification with Deep Learning: A Study on the Effectiveness of Pretrained Models

Abstract

The rapid growth in population, urbanization, and economic activities has led to a significant increase in solid waste generation, raising critical environmental concerns. Proper disposal and recycling of municipal solid waste are vital for sustainable development, aligning with the United Nations Sustainable Development Goals. Previous studies in this domain faced challenges such as the unavailability of real-world waste data and insufficient model generalization for diverse waste categories. This study explores the optimization of solid waste classification by employing transfer learning with pre-trained convolutional neural network models to address these limitations. The study utilizes the RealWaste dataset, consisting of 4,752 images categorized into nine waste classes (paper, plastic, glass, vegetation, food organics, cardboard, textile trash, metal, and miscellaneous trash), to assess the performance of three pre-trained convolutional neural network models: ResNet50, DenseNet121, and EfficientNet-B0. Originally trained on the ImageNet dataset, these models were fine-tuned to classify waste materials. The ResNet50 model achieved an impressive training accuracy of 100% and a validation accuracy of 92.24%, followed by DenseNet121 with a training accuracy of 99.99% and a validation accuracy of 91.82%, and EfficientNet-B0 with a training accuracy of 99.83% and a validation accuracy of 91.19%. Among the evaluated models, DenseNet121 achieved the highest test accuracy of 94.33%, followed by ResNet50 at 91.82% and EfficientNet-B0 at 91.4%, demonstrating the superior performance of DenseNet121 in the waste classification task. These results underscore the capability of deep learning to automate waste categorization with high precision. The research findings highlight the potential of transfer learning with pre-trained convolutional neural networks for reliable waste classification across multiple categories, fostering efficient recycling systems and contributing to a circular economy, thereby supporting global sustainability efforts.