YOLO-Driven Smart System For Waste Detection and Classification

Abstract

The growing global waste crisis, exacerbated by rapid urbanization and population growth, demands innovative and efficient solutions. Traditional waste management methods often struggle to handle the increasing volume of waste, resulting in inefficiencies, environmental degradation, and public health issues. This paper presents a smart waste management system that utilizes artificial intelligence (AI) and machine learning (ML) technologies, specifically YOLOv8 grid segmentation, to enhance waste classification and management. The system is capable of processing both still images and videos to classify various types of waste, including paper, metal, plastic, glass, and general waste, providing accurate classification results along with percentage accuracy. These real-time waste classification features are integrated into a user-friendly website, allowing users to upload images or videos for detection and classification. Additionally, the system tracks the quantity of each type of waste, enabling interested parties to purchase specific waste types and thereby supporting a circular economy. By applying predictive analytics to historical data, population density, and seasonal trends, the system can accurately forecast waste generation and optimize the entire waste management lifecycle from generation to disposal. This smart waste management solution not only enhances the efficiency of waste collection and recycling but also lowers operational costs and reduces environmental impact, significantly contributing to the development of sustainable cities and communities.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), YOLOv8, Grid Segmentation, Waste Classification, Predictive Analytics, Sustainable Waste Management, Circular Economy, Real-Time Monitoring, Environmental Sustainability, Waste Tracking.