Abstract: 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.

Keywords: Deep Learning, YOLOv8, Waste Management, Garbage Classification, Object Detection, Real-time, Environmental Monitoring.

| DOI: 10.17148/IARJSET.2024.11317