Investigating State of the Art Hyperspectral Imaging Classification Models for Plastic Types Identification

This study aims to enhance the robustness and real-time applicability of polymers classification for more efficient and sustainable recycling practices. By utilizing a hyperspectral imaging (HSI) plastic dataset in the visible/near and the short wave infrared acquired at the Helioslab at the Helmholtz In-stitute Freiberg for Resource Technology, we assess multiple state-of-the-art (SOTA) machine learning (ML) models in-cluding single and multi-modality Transformers-based models like Vision Transformer, SpectralFormer, and Multimodal Fusion Transformer for polymer identification in challenging industrial scenarios. We investigate the impact of different wavelength ranges on the models' performance by ex-amining four wavelength ranges related to the polymers' ab-sorption features. We propose an enhanced HSI data prepro-cessing pipeline targeting close and long-range hyperspectral applications to handle large hyperspectral data and its aug-mented version. The comprehensive experiments and fine-tuning demonstrate the proposed pipeline's efficiency, signif-icantly enhancing the SOTA performance. The work will be available at https://github.com/hifexplo.