An Intelligent Plastic Waste Classification System Based on Deep Learning and Delta Robot

Abstract

This paper proposes an intelligent plastic waste classification system based on the Deep Learning model and Delta robot. This system includes a Delta robot, a camera, a conveyor, a control cabinet, and a personal computer. The system applies Transfer Learning with the pre-train YOLOv5 model to detect plastic waste in real-time. The best model is selected with the best weight by evaluating the results of the pre-train model to classify different types of plastic waste and determine the positions of the waste by Bounding box. Then, these positions are converted into the Delta robot’s coordinate system by the formula obtained from the transformation matrix and the position of the camera. Finally, the computer processes and transports data to control the Delta robot to classify plastic waste in the conveyor. Afterward, a variety of classification experiments with more than 1000 samples in two different lighting conditions were conducted. The results illustrate that the computer vision and deep learning model achieve excellent efficiency with the best-performing case having a Precision of 96% and a Recall of 97%. In conclusion, the experimental results in this paper demonstrate that the proposed intelligent plastic waste classification system delivers high performance both in terms of accuracy and efficiency and has much more potential for further development.