Automatic Garbage Classification Using YOLOV8

Garbage classification plays a crucial role in waste management.The existing method employs Convolutional Neural Networks (CNN) for garbage classification, providing accurate but computationally intensive results.This paper proposes the integration of YOLOV8, a state-of-the-art object detection algorithm, for real-time garbage classification categorization(like paper, cardboard, biological, metal, plastic, green-glass, brown-glass, white-glass, clothes, shoes, batteries, and trash) through live camera feed analysis.The proposed YOLOV8 model aims to address this limitation, optimizing both accuracy and speed for live garbage detection.