A machine vision based autonomous waste managment system using deep learning

Author(s)	Prof. Kalpana Dongare, Ms. Bhakti Thombare
Country	India
Abstract	Rapid urbanization has intensified the challenges of waste management, as traditional garbage collection methods remain inefficient, labor-intensive, and prone to improper segregation. This project proposes an AI-based Garbage Sorting and Monitoring System that uses the YOLOv5n object detection model to automatically classify waste into categories such as plastic, paper, glass, metal, organic, and e-waste. The lightweight YOLO architecture enables real-time detection with minimal computational resources, supported by a custom annotated waste image dataset. Detected data is visualized through an analytics dashboard, offering real-time insights into waste types, frequency, and recycling potential to assist smart city decision-making. The system is scalable and can be integrated with IoT-based smart bins or robotic collectors, contributing to automated, data-driven, and sustainable waste management solutions.
Keywords	Garbage Detection, YOLOv5n, Waste Classification, Analytics Dashboard, Computer Vision, Smart City Waste Management.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.59669

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