Masked Signal Modeling for Plastic Waste Resin Classification

Near-infrared spectroscopy is a viable option for plastic material detection. The lack of diversity and scale of such data makes it difficult to translate the research outcomes into industrial settings. In this paper, we collect a diverse plastic dataset, named AI Recycling Plastics (AIRP), with different compositions of clean and contaminated plastics. Moreover, we aim to apply self-supervised pre-training using masked signal modeling (MSM) to improve the performance of deep learning models in detecting the plastic resin types from the spectral signature data. Our results show that MSM, as a pre-training task, teaches the model to learn rich features that generalize well from unlabeled spectra data. Fine-tuning performance in the detection of plastic resin type outperforms the baseline supervised techniques. Our proposed approach shows promising results that can be applied industrially at material recovery facilities to sort recyclable plastics and less common polymer types.