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Dataset for automated material flow characterization of shredded WEEE: RGB-camera-based object images and mass data

dc.contributor.authorVogelgesang, Malte
dc.date.accessioned2025-04-16T10:22:59Z
dc.date.available2025-04-16T10:22:59Z
dc.date.issued2024
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4560
dc.identifier.urihttps://doi.org/10.48328/tudatalib-1743
dc.descriptionThree datasets containing data from particles of shredded WEEE, including ferrous metals, non-ferrous metals, plastics and printed circuit boards in two particle size ranges of 12.5 mm - 25 mm and 25 mm - 50 mm, split into image data and mass data. - Dataset 1 contains images that were used to train and test convolutional neural networks to identify the four material types through image classification, object detection, and instance segmentation. Additionaly, the total mass per material type and particle size range is included. - Dataset 2 contains images and corresponding particle masses that were used in the training and testing of regression models for particle mass prediction. - Dataset 3 contains images of particles from three predefined mixed samples, together with the total mass per material type and particle size range in each sample. The images were recorded with an industry-sized sensor-based sorting machine (Sesotec Varisort Compact [Schoenberg, Germany]) at the pilot-scale sorting plant at Fraunhofer IWKS in Alzenau. Computer vision was used to extract individual particles from the recorded image stream. The masses were recorded using a precision balance (KERN & SOHN EWJ 3000-2 [Balingen, Germany]) at the output conveyor.de_DE
dc.rightsCreative Commons Attribution 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectWEEEde_DE
dc.subjectShredded e-wastede_DE
dc.subjectElectronic wastede_DE
dc.subjectMachine learningde_DE
dc.subjectMaterial type identificationde_DE
dc.subjectParticle mass predictionde_DE
dc.subjectSensor-based material flow characterizationde_DE
dc.subjectImage datade_DE
dc.subjectParticle massesde_DE
dc.subjectFerrous metals, non-ferrous metals, plastics, printed circuit boardsde_DE
dc.subject.classification4.21-03 Mechanische Verfahrenstechnikde_DE
dc.subject.classification4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahrende_DE
dc.subject.ddc004
dc.subject.ddc660
dc.titleDataset for automated material flow characterization of shredded WEEE: RGB-camera-based object images and mass datade_DE
dc.typeDatasetde_DE
tud.unitTUDa


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Creative Commons Attribution 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0