Garbage Classification: A Deep Learning Perspective

Abstract

Garbage Classification using deep learning focuses on techniques to automate and improve the sorting of waste materials. The objective is to enhance recycling processes and promote  environmental sustainability by accurately categorizing waste into six types: glass, paper, cloth, trash, cardboard, and plastic. The study implements advanced convolutional neural networks (CNNs) to analyze and classify images of garbage, automating a task that is traditionally manual and labor-intensive.To achieve this, several deep learning models were used, including MobileNet, NASNet, LeNet, Inception, and DenseNet. These models were trained on a carefully curated dataset to ensure balanced representation across all waste categories, allowing them to extract complex features from the images and make precise classifications. Each model was evaluated based on its performance, with NASNet delivering the highest accuracy, making it the most suitable for real-world applications where resources might be limited, such as mobile or edge devices.The results demonstrate that NASNet is the most effective algorithm for garbage classification, outperforming the other models in terms of accuracy. By automating the classification process, this research offers a practical solution to improve recycling efficiency, reduce the need for manual sorting, and contribute to sustainable waste management. The study highlights the significant role that deep learning can play in transforming waste management systems for a cleaner and more sustainable environment.