Smart Waste Classification System using YOLO Framework
Deepika Boragaonkar, Rahul Muppa, Abhiram Padamatinti, Sreekar Pothu · 2025
Effective waste management is a crucial approach to conquering global environmental challenges. This project employs the YOLOv11 framework to work on a real-time waste categorization system that can classify waste into different types ranging from plastics, metals, paper, and organics. The system utilized the superior image classification, processing capabilities of YOLOv11 to provide immediate and accurate results. The intuitive interface allows users to upload images of waste that the system assigns a category label to and provides visual demarcation boxes to give more understanding. Besides classification, the system also generates analytics such as statistics on waste distribution toward the implementation of sustainable management of waste. With high accuracy and speed, YOLOv11 is scalable for various application cases, from individual house applications to industrial sorting processes. Future work can include the integration of IoT-enabled smart bins for the automation of waste segregation and increasing the dataset to cover more diverse types of waste, such as hazardous materials. This project illustrates the potential of deep learning technologies in fostering sustainability through the automation and optimization of waste management workflows, hence supporting environmental conservation globally.