Synthetic Data Generation for Efficient Waste Sorting in Industrial Recycling

The growing global waste situation, especially involving recyclable materials like aluminum cans, transparent bottles, and milk cartons, emphasizes the need for efficient waste sorting systems. However, the lack of diverse, high-quality datasets limits machine learning models' performance in realworld recycling scenarios. This study addresses this challenge by generating a synthetic dataset using Blender, simulating diverse environmental conditions such as lighting, backgrounds, and object orientations. The generated dataset also automatically outputs the labels corresponding to the bounding boxes of the location of each object in each image, eliminating the need for manual labeling. We used the YOLOv11 object detection model on our generated dataset. The model demonstrated strong performance in detecting and classifying recyclable objects of varying sizes and complexities. This approach showcases the potential of synthetic data to train robust models, reducing reliance on labor-intensive data collection and improving recycling efficiency to advance sustainable waste management techniques.