Abstract:

In recent years, the increasing volume of waste generated globally has posed significant challenges to urban management and environmental sustainability. This study presents a deep learning-based approach to automate garbage classification using Convolutional Neural Networks (CNNs). The model was trained on a diverse dataset comprising five distinct classes: paper cups, paper, clothes, metal, and plastic. With an impressive accuracy of 96% during testing, the results demonstrate the potential of CNNs in recognizing and classifying various types of waste, which can facilitate efficient waste management practices. This work addresses the need for improved waste sorting mechanisms and contributes to environmental conservation efforts by promoting recycling and reducing landfill usage. The findings indicate that implementing AI-driven solutions can significantly enhance the efficacy of waste management systems.

Problem Statement:

The increasing volume of waste generated in urban areas presents a significant challenge for effective waste management. Manual sorting of waste is not only labor-intensive and time-consuming but also prone to inaccuracies, leading to improper disposal and reduced recycling rates. Traditional waste management systems struggle to keep pace with the growing complexity of waste types, resulting in a negative impact on environmental sustainability. This project aims to develop an automated garbage classification system using Convolutional Neural Networks (CNNs) to accurately categorize waste into five classes: paper cups, paper, clothes, metal, and plastic. By improving the efficiency and accuracy of waste sorting, this system seeks to facilitate better recycling practices and contribute to a more sustainable waste management approach.


Objective:

The primary objective of this study is to develop a highly accurate classification model for waste sorting, leveraging Convolutional Neural Networks (CNNs) to categorize waste into five distinct classes: paper cups, paper, clothes, metal, and plastic. The goal is to achieve a classification accuracy of at least 95%, ensuring reliable performance for practical applications. To improve model accuracy, image preprocessing techniques such as resizing and normalization are applied, refining the quality of the input data and thus optimizing model training and testing results. Comprehensive performance evaluations are conducted, including accuracy metrics, confusion matrix analysis, and ROC curve assessments, to gauge the model's effectiveness in accurately identifying each category of waste.

In pursuit of optimal model performance, the study explores various CNN architectures and hyperparameters, including layer configurations, filter sizes, and dropout rates, identifying the most effective setup to maximize classification accuracy. Additionally, the implementation of real-time prediction capabilities is a critical objective, enabling the trained model to classify waste images captured directly through a camera interface in a user-friendly application. This approach not only supports efficient waste sorting but also aims to raise awareness about recycling and proper waste disposal, helping users identify and sort materials effectively.