Increasing the reuse of wood in bulky waste using artificial intelligence and imaging in the VIS, IR, and terahertz ranges
Lukas Roming,
Robin Gruna,
Jochen Aderhold,
Friedrich Schlüter,
Dovile Cibiraite -Lukenskiene,
Dominik Gundacker,
Fabian Friederich,
Manuel Bihler,
Michael Heizmann
Kapitel/Beitrag aus dem Buch: Beyerer, J et al. 2023. OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings.
Bulky waste contains valuable raw materials, especially
wood, which accounts for around 50% of the volume. Sorting
is very time-consuming in view of the volume and variety of
bulky waste and is often still done manually. Therefore, only
about half of the available wood is used as a material, while the
rest is burned with unsorted waste. In order to improve the material
recycling of wood from bulky waste, the project ASKIVIT
aims to develop a solution for the automated sorting of bulky
waste. For that, a multi-sensor approach is proposed including:
(i) Conventional imaging in the visible spectral range; (ii) Nearinfrared
hyperspectral imaging; (iii) Active heat flow thermography;
(iv) Terahertz imaging. This paper presents a demonstrator
used to obtain images with the aforementioned sensors. Differences
between the imaging systems are discussed and promising
results on common problems like painted materials or black
plastic are presented. Besides that, pre-examinations show the
importance of near-infrared hyperspectral imaging for the characterization
of bulky waste.