International Journal of Science, Engineering and TechnologyAuthors: Atharva Subhash Patil, Pritish Narayan Patil, Dr. Jasbir Kaur, Ifrah Kampoo, Mansi Rajapurkar
Abstract: The rapid growth of urban waste has created a major challenge for environmental sustainability and waste management operations, especially in cities where segregation is performed manually and often inconsistently. Manual sorting is slow and vulnerable to human error, contamination, and operational inefficiency, particularly when multiple waste categories are mixed together. To address this issue, this paper presents a real-time waste classification framework based on YOLOv8, combined with environmental impact feedback through decomposition-time information. The model was trained using a mixture of synthetic and real waste images covering six waste classes: plastic, paper, metal, glass, organic waste, and cardboard. YOLOv8 was selected because it is designed for real-time object detection and supports standard evaluation using precision, recall, and mean Average Precision metrics. The model was further fine-tuned on real-world waste images to improve robustness in practical conditions such as clutter, illumination changes, partial occlusion, and background variation. Experimental results showed a validation mAP@0.5 of approximately 99.4%, with precision and recall near 99%. In addition to detection, the system presents decomposition-time and disposal guidance, which helps increase environmental awareness and encourages responsible waste handling.
