An enhanced garbage image classification using deep learning Techniques.

Abstract

The increasing volume of waste generated globally has necessitated the development of efficient waste management systems. Traditional methods of waste sorting are labor-intensive, time-consuming, and prone to human error. In this research a deep learning-based approach for automated garbage image classification was developed, focusing on six distinct categories: metal, paper, organic, battery, plastic and glass. Leveraging convolutional neural networks (CNNs), the system aims to accurately classify waste materials from images, thereby facilitating efficient recycling and waste management processes. The dataset used for training and evaluation consists of labeled images of various waste types, preprocessed and augmented to enhance model performance. Experimental results demonstrate the effectiveness of the proposed model in achieving high classification accuracy, highlighting its potential for real-world deployment in smart waste management systems. This research contributes to the growing field of environmental sustainability by providing a scalable and automated solution for waste classification.