Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for
effective recycling and progress toward a circular economy. Achieving this transformation requires higher
sorting intensity through intensified processes, increased efficiency, and enhanced yield.
While manual sorting remains common, smaller plants often use positive sorting to recover recyclables, and
larger plants combine automated systems with manual sorting. Negative sorting is employed to remove impurities
and improve material quality. However, innovation in manual sorting has stagnated. Advances in Machine
Learning and Artificial Intelligence offer transformative potential for waste management, with digitalisation and
improved recyclate quality becoming priorities. Despite these trends, manual sorting is still largely treated as a
digital black box.
The presented research outlines the design of a novel, human-centric AI-powered assistance system to support
sorting workers by enhancing decision-making and real-time assistance during the sorting process, driving the
digitalisation of manual sorting. Potential use cases, system requirements, and essential components were
explored. High-quality use case-specific data is essential for model training. Therefore, publicly available datasets
were evaluated but found inadequate, necessitating use-case-specific data acquisition through near-industryscale
experiments. This data was used to train and develop key system components, such as object recognition,
classification, and action recognition models. Results indicate that transfer learning with a balanced dataset
is effective for waste-sorting applications. The classification model achieved 81% accuracy on an experimental
acquired balanced dataset, outperforming the accuracy of the pre-trained model on its original dataset.