Abstract
In Germany Bio waste is collected in separate garbage bins from households in the municipalities (e.g. garden waste, kitchen waste, etc.) and composted. The end result is humus, which is finally fed back into agriculture and closes the organic materials cycle. Waste must be inspected for non-biological contaminants prior to composting, as these can compromise the composting process and damage screening equipment at the recycling facility. Undetected contaminants affect the quality of the humus and can lead to contaminants re-entering the food chain through agriculture. The paper presents a feasibility study of an automatic bio waste Contamination-Scanner aiming to catch contamination early in the recycling process. Image data of bio waste contamination has been collected from a recycling facility. These images were used to design, train and evaluate two Convolutional Neural Networks (CNNs) aimed at detecting contaminants during bio waste collection. One CNN was trained on RGB and the other on greyscale images. The results show an initial surface scan can detect contamination with an accuracy of up to 86% and could form part of a holistic detector attached to bin lorries.
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Acknowledgemenets
We would like to thank Wolfgang Schöning from RETERRA GmbH and his colleagues for their explanations on the bio waste recycling process and the impacts of contaminants. We also thank Mattis Wolf from DFKI for valuable pointers to the effective training of CNNs.
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Stahl, F., Ferdinand, O., Nolle, L., Pehlken, A., Zielinski, O. (2021). AI Enabled Bio Waste Contamination-Scanner. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_28
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DOI: https://doi.org/10.1007/978-3-030-91100-3_28
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