Intelligent Waste Sorting System Using Deep Learning

The escalating global waste management crisis necessitates effective solutions to manage the increasing volume of municipal solid waste while reducing environmental harm. We introduce an innovative Intelligent Waste Sorting System (IWSS), utilizing advanced deep learning techniques to automate and enhance waste sorting efficiency. By integrating the cutting-edge YOLOv7 object detection model, our system achieves an impressive 92% accuracy in categorizing waste into five distinct types: paper, plastic, glass, metal, and others. Trained on a comprehensive dataset of waste images, the IWSS operates as a purely software-based solution, eliminating the need for extra hardware, and ensuring accessibility and cost-effectiveness. Our system demonstrates adaptability through real-time learning, continuously refining its performance without needing hardware modifications. This software-only approach improves waste management efficiency by reducing reliance on manual sorting processes, promotes recycling, and minimizes environmental impact. Through rigorous experimentation and validation, we highlight our system’s effectiveness and reliability in tackling municipal solid waste management challenges. The IWSS marks a significant step forward in sustainable waste management practices, offering practical applications in both urban and rural environments and contributing to a cleaner, greener future.