Development of a Deep Learning Based Automatic Garbage Classification System

Abstract

Abstract: The escalating global waste production has given rise to significant environmental challenges, encompassing pollution and health risks. Effective waste management hinges on the essential process of garbage categorization. Nonetheless, manual sorting proves to be arduous, error- prone, and time-intensive. To address this, automated garbage classification powered by deep learning has emerged as a promising remedy. This project introduces a garbage classification framework leveraging convolutional neural networks (CNNs) to achieve remarkable precision in distinguishing various garbage types. Comprising preprocessing, feature extraction, and classification stages, this system holds the potential to curtail the necessity for manual intervention in waste management operations. This, in turn, can enhance the efficiency and precision of garbage segregation procedures