The biggest challenge in waste sorting is the recognition and classification of waste, which can be done either directly, based on physical properties, or indirectly, using a camera. The latter usually involves the use of neural networks trained on a prepared training set to recognize objects in the image. We trained the YOLO algorithm on a publicly available dataset called TACO. For testing purposes, we created a test robotic cell by developing a soft pusher, mounting it on the collaborative robot Fanuc CR7iA/L and developed a web application for tracking and managing the sorting process.
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