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

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.