Revolutionizing Waste Management: Leveraging YOLOv8 for Enhanced Waste Categorization

The rapid growth of industries, urban areas, and the global population has led to significant environmental damage, particularly through waste accumulation. This research project addresses the urgent problem of waste pollution by employing advanced computer vision techniques, specifically focusing on identifying and categorizing various types of waste using the YOLOv8 deep learning framework. The main objective of the project is to develop an efficient waste categorization system capable of sorting complex mixtures of garbage, thereby enhancing recycling effectiveness and reducing reliance on human labor. A key aspect of this research is evaluating YOLOv8's mean average accuracy (mAP) across diverse datasets of waste items captured in their natural settings, while considering different environmental conditions. The study also compares YOLOv8 with its predecessors, highlighting its potential as a precise and effective alternative to traditional, labor-intensive waste management methods. The model development involved five steps, each focusing on refining the model and augmenting data at various levels. The resulting metrics—a mAP50 of 84.1\%, an overall precision of 93\%, and a recall of 68.7\%—demonstrate the model's strong performance after extensive iterations. The findings and insights from this research could significantly transform waste management practices, positively impacting the environment, public health, and the economy.