Optimizing recycling using automated image-based classification

: a machine learning approach for improved waste management

Abstract

This thesis covers image-based waste classification using machine learning and discusses its impact on sustainable waste management. To identify the optimal model, the prediction performance of a DenseNet, a state-of-the-art Convolutional Neural Network, and DAtNet model are examined and compared to each other. The DAtNet integrates attention layers on the DenseNet architecture, inspired by the transformer model, known for its success in large language models. Moreover, the impact of transfer learning and augmentation on the test accuracy is analyzed. The performance of these models is evaluated across multiple datasets to exam ine their generalization capabilities. The findings indicate that while the DAtNet surpasses the DenseNet model in accuracy with large datasets, it faces difficulties with smaller datasets and requires significantly more time to preprocess the images. In contrast, the DenseNet consistently performs well and processes images more efficiently. Therefore, a DenseNet model is recommended for waste management fa cilities due to its reliability and lower computational demands. However, the further investigation and improvement of attention layers is encouraged. Additionally, the development of more practical, representative datasets is essential for the effective implementation of machine learning models in real world waste management. The deployment of this work could support the achievement of Sustainable Development Goals and the realization of zero-waste cities.

Date of Award	4 Jul 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Afonso Fernandes (Supervisor)