AI-BASED WASTE CLASSIFICATION AND ROUTING SYSTEM: SMART CAMPUS APPLICATION

Abstract

This study presents an intelligent waste sorting system designed to accurately and efficiently classify waste using artificial intelligence and image processing technologies. Equipped with cameras and sensors, the system employs a YOLOv5-based object detection algorithm to identify plastic, paper, metal, glass, and organic waste in real time with high accuracy. IR-supported lighting enhances performance under low-light conditions, while collected data are transmitted to a central server to ensure traceability. The digital feedback interface improves user experience, and energy-efficient hardware reduces operational costs. Achieving a 91% accuracy rate and 28% energy savings, the system outperforms existing solutions. Applicable in university campuses, municipalities, and industrial areas, it integrates IoT technology for fill-level monitoring and data management, optimizing waste collection processes. Thus, the study contributes to environmental sustainability by providing a smart environmental management solution.