AI-Powered Smart Waste Management and Complaint Redressal System Using YOLOv8

Abstract

Rapid urbanization and increasing population density have significantly intensified the challenges associated with efficient waste management and environmental sustainability. Traditional waste disposal practices often lack proper segregation, monitoring, and timely response mechanisms, resulting in environmental degradation and health hazards. To address these issues, this paper presents an AI-powered smart waste management and complaint redressal system that integrates real-time object detection, data analytics, and citizen engagement into a unified platform. The proposed system utilizes the YOLOv8 deep learning model for real-time waste detection through webcam input and image uploads. Detected waste items are automatically classified into categories such as organic, recyclable, hazardous, and electronic waste, and are mapped to appropriate disposal bins. The system further stores detection data along with contextual information such as location, timestamp, and recyclability status, enabling structured data analysis. The In addition to detection capabilities, the system incorporates an administrative dashboard that provides visual insights through charts and statistics, assisting authorities in understanding waste distribution patterns. A report generation module is also included to produce official documents for monitoring and decision-making. Furthermore, a complaint management feature allows citizens to report waste-related issues with images and location tracking, facilitating timely intervention by municipal authorities. The integration of artificial intelligence with data-driven decision support and user participation makes the proposed system a scalable and practical solution for modern smart cities. The results demonstrate improved efficiency in waste classification, better tracking of waste trends, and enhanced responsiveness to citizen complaints.

Introduction

Waste management has become a major challenge due to rapid urbanization, industrialization, and increasing waste generation. Traditional waste management methods rely heavily on manual segregation and monitoring, making them inefficient, time-consuming, and prone to errors. To address these challenges, this project proposes an AI-powered Smart Waste Management System that integrates YOLOv8-based real-time waste detection, data analytics, and citizen participation into a single web-based platform.

The system automatically detects waste from live webcam feeds or uploaded images, classifies waste into categories such as organic, plastic, e-waste, and medical waste, and recommends the appropriate disposal bin. It also provides text-to-speech guidance for users, stores detection records in a database, and generates analytics dashboards and PDF reports for administrative decision-making. A complaint management module enables citizens to report waste-related issues with images and GPS-based location data, improving communication between the public and municipal authorities.

The literature review highlights that previous AI-based waste classification systems using CNNs and transfer learning achieved high accuracy but suffered from limitations such as high computational requirements, limited waste categories, lack of real-time detection, and poor user interaction. While YOLO models offer faster and more accurate real-time detection, existing solutions often fail to integrate complaint management, analytics, and administrative features. The proposed system overcomes these limitations by combining AI detection, geolocation-based complaint handling, and data visualization within a unified platform.

The methodology includes data acquisition from webcams and uploaded images, YOLOv8-based object detection, waste classification with bin mapping, text-to-speech feedback, complaint management, database storage using SQLite, an analytics dashboard, and automated PDF report generation. The modular web-based architecture ensures scalability, accessibility, and efficient management of waste-related data.

Experimental results demonstrate that the system accurately detects various waste types, including plastic bottles, metal cans, electronic waste, organic waste, and hazardous materials. Detection records are securely stored with details such as waste type, recyclability, location, and timestamp. The analytics dashboard provides visual insights through charts and graphs showing waste distribution, recycling rates, and detection trends, while automated PDF reports support municipal planning and decision-making. Overall, the proposed system offers an intelligent, transparent, and efficient solution for modern waste management, contributing to cleaner cities and supporting smart city initiatives.

Conclusion

This The proposed AI-powered smart waste management and complaint redressal system successfully demonstrates the potential of integrating artificial intelligence with real-world environmental applications. By combining real-time object detection, structured data management, analytics, and user interaction, the system provides a comprehensive approach to addressing modern waste management challenges. The implementation of the YOLOv8 model enables accurate and efficient waste classification, reducing dependency on manual segregation and minimizing human error. The storage of detection data along with location and time information allows for detailed analysis of waste patterns, which can be utilized for improving waste collection strategies and resource allocation. The inclusion of an analytics dashboard enhances decision-making by presenting data in a visual and interpretable format. Additionally, the automated report generation feature simplifies documentation processes and supports communication with higher authorities. The complaint management module further strengthens the system by enabling direct citizen participation, ensuring that waste-related issues are reported and addressed promptly. Overall, the system provides a scalable and practical solution for smart cities aiming to improve waste management efficiency and environmental sustainability. Future enhancements may include integration with IoT devices, deployment on mobile platforms, and expansion of the detection model to cover a wider range of waste categories. The project highlights the importance of combining technology with user-centric design to create impactful and sustainable solutions.

References

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Copyright

Copyright © 2026 Asad Khan Sarguru, Mohd Nayeem Dalwai, Adesh Pandey, Mrs. Archana Kalia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.