Garbage management is an important environmental challenge that requires efficient methods for identifying and segregating different types of waste. Manual garbage identification and sorting are time-consuming and may result in improper waste disposal. This project proposes an AI-based Garbage Detection System using computer vision and deep learning techniques to automatically detect and classify waste objects from images. The system uses a YOLOcompatible garbage detection dataset containing 10,464 labeled images divided into training, validation, and testing sets. The dataset consists of six major waste categories: biodegradable, cardboard, glass, metal, paper, and plastic. A YOLObased object detection model can be trained on these annotated images to identify the location and category of garbage objects. The proposed system can assist in automated waste segregation, smart recycling systems, and environmental monitoring. By reducing dependence on manual sorting, the system can improve the efficiency of waste management and encourage proper recycling practices. The project demonstrates how artificial intelligence and computer vision can be applied to develop an intelligent and sustainable garbage management solution.

Selvaranjani R U, Subha Harini R, Vishali G, Vipin Vishal A, Dr. Pradeepa K,"YOLOv8-Garbage Detection" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(573-577) July - August 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.