AI-Powered Real-Time Waste Classification Using YOLOv7 for Smart City Networks

This research introduces a pioneering real-time smart waste management system utilizing computer vision, specifically targeting household waste. Amidst escalating environmental concerns and the limitations of traditional waste management systems, this project aims to revolutionize waste management practices through the integration of advanced technologies. At its core lies YOLOv7, a state of the art deep learning framework renowned for its efficiency in object detection tasks. By leveraging YOLOv7, the system facilitates precise waste identification, classification, and monitoring in real-time, addressing the inefficiencies and environmental impacts of conventional waste management approaches. The urgency of such innovation is underlined by the challenges posed by urbanization, population growth, and changing consumption patterns. This research not only offers immediate solutions but also envisions a future where waste management processes are optimized, tailored to diverse communities, and environmentally sustainable. The methodology involves designing system architecture, collecting diverse data, training the YOLOv7 model, and implementing it in real-world scenarios. Empirical evaluations validate efficacy and feasibility, ensuring practical deployment readiness. Ultimately, this research signifies a significant step towards a more sustainable, efficient, and environmentally conscious approach to waste management.