Real-Time Waste Classification Using YOLOv8n: A Deep Learning Approach
Abstract
This study presents an artificial intelligence-based approach to real-time waste classification using a lightweight version of the YOLOv8 object detection model. The environmental impact of improper waste management is significant, with increasing volumes of waste in urban areas posing serious challenges. Through the use of deep learning and computer vision, this research aims to enable efficient, automatic classification of solid waste into ten distinct waste classes. A labeled image dataset was curated and preprocessed using Roboflow [8] and various data augmentation strategies. The YOLOv8n model was trained and evaluated using precision, recall, F1 score, and mean Average Precision (mAP) metrics. Results demonstrate that the proposed system achieves high accuracy and real-time performance, making it suitable for deployment in smart bins, mobile waste collection systems, and urban surveillance applications. These outcomes include a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.98, precision of 0.95, and recall of 0.98. The study contributes to the growing body of AI applications in environmental sustainability by offering a scalable and practical solution to address solid waste classification challenges in real-world settings.
Keywords
References
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Details
Primary Language
English
Subjects
Artificial Intelligence (Other)
Journal Section
Research Article
Publication Date
January 30, 2026
Submission Date
July 8, 2025
Acceptance Date
January 3, 2026
Published in Issue
Year 2026 Volume: 2 Number: 1