Development of a deep learning-based object recognition system for pre-stage separation to improve the recycling rate of major general-purpose plastics

초록

The generation of plastic waste has been increasing annually, necessitating various recycling methods. Among these, material recycling with low carbon emissions should be prioritized. This study aims to enhance the material recycling rate by developing a separation system using deep learning-based object recognition. To improve labeling efficiency, image data were acquired for each material type, and the performance of different labeling methods was evaluated. The average precision (AP) values for the single-material learning model using hybrid labeling (manual + automatic) were 0.947 for PET, 0.951 for PP, 0.892 for PE, and 0.896 for PS, demonstrating superior performance compared to other methods. An integrated learning model was also developed for composite materials, achieving AP values of 0.972 for PET, 0.976 for PP, 0.963 for PE, and 0.961 for PS. These results demonstrate the model's strong applicability for plastic waste recognition.

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