Environmental Sustainability App (GreenWayFinder) : Waste Image Detection and Management System Integrating Deep Learning and Geospatial Analysis.

Abstract

"Abstract In contemporary society, the pressing environmental challenge of improper waste disposal necessitates innovative solutions. The inefficiency in waste management and the lack of accessible information for individuals seeking to recycle contribute to environmental degradation. Our project aims to address this issue by developing an intelligent mobile application utilizing deep learning components. The primary problem identified is the lack of a streamlined system for users to easily identify recyclable items and locate nearby recycling centers that accept specific materials. This gap hinders the widespread adoption of recycling practices, hindering progress towards a more sustainable future. The proposed solution integrates Convolutional Neural Networks (CNN) for image recognition and geospatial analysis, allowing users to capture images of recyclable items and receive real-time information on the nearest recycling facilities accepting those materials. Through this, our project seeks to enhance recycling accessibility and contribute to a more eco-friendly waste management paradigm. Our methodology encompasses a dual-pronged approach to address the identified waste management challenge. To achieve precise image recognition, we employ Convolutional Neural Networks (CNN) and integrate the YOLO v8 model from Ultralytics. This ensures real-time and accurate identification of recyclable items from user-provided images, forming a robust foundation for waste detection. Simultaneously, we leverage geospatial analysis by utilizing FastAPI for efficient communication between the frontend and backend. This enables the app to determine the user's location through geolocation data and cross-reference it with a comprehensive database of recycling facilities. The outcome is a seamless integration of advanced image recognition and geospatial intelligence, empowering the application to not only identify recyclable items but also recommend the nearest recycling centers equipped to handle those materials. This innovative methodology positions our project as a comprehensive solution to enhance recycling accessibility and contribute to a more sustainable waste management ecosystem. The initial results of our prototype showcase a promising and efficient waste detection and management system. Leveraging the YOLO v8 model for image recognition and geospatial analysis, our prototype accurately identifies recyclable items from user-provided images in real-time. The integration of FastAPI and Streamlit ensures a user-friendly experience, allowing seamless contributions through both file uploads and real-time camera captures. The prototype successfully processes data, providing users with actionable information on nearby recycling centers that accept specific materials. In terms of quantitative assessment, the classification performance metrics, such as Confusion Matrix and AUC-ROC, demonstrate the reliability and accuracy of our image recognition system. The initial implementation establishes a solid foundation, promising further refinement and feature enhancements as the project progresses, setting the stage for a sophisticated waste detection solution. "