Computer vision methods have been recently integrated into industrial facilities performing the automatic household waste sorting. In this research, we report on an approach based on hyperspectral cameras and consecutive data analysis in recycling plants for improve waste sorting processes in terms of waste detection and classification. The majority of existing methods have been conducted on static conveyor belt, and their findings may not be applicable to recycling systems. The study focuses on developing a real-time hyperspectral object classification system for sorting machines, addressing the challenge of differentiating objects that are unidentifiable to RGB cameras. The linear hyperspectral camera operates within the wavelength range of 900 nm to 1800 nm. The research includes the creation of a dataset with physically separated training and test data, automated pseudo-labeling of hyperspectral data. This allows for rapid expansion to new classes without waste of manual annotation. The dataset contains 18 classes of objects, including various types of plastics, films, textiles, tetrapack, and cellulose products. To improve the accuracy and inference speed, we experiment with normalization, dimensionality reduction, and model calibration. Experimental results show a promising F1-score of 71% on a moving conveyor belt.
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