Material flow composition dataset: RGB-camera-based single-particle images and particle mass data of shredded WEEE
Abstract
Mass-based material flow compositions (MFCOs) are among the most relevant indicators for the quality of sorting processes in waste management. They provide the basis for the assessment and improvement of the purity and yield of material streams. While MFCOs are currently primarily determined through labor-intensive manual sorting analyses, recent research has explored sensor-based material flow characterization (SBMC). Near-infrared spectroscopy (NIR) is effective for plastic waste but cannot reliably distinguish many material types in shredded waste electrical and electronic equipment (WEEE). Therefore, this article aims to provide data for research on SBMC with more versatile and often already implemented RGB cameras in sorting plants. The generated data contain shredded WEEE, which was recorded on a sensor-based sorting machine on an industrial scale. The samples were taken from an industrial WEEE recycling facility in Germany and manually presorted into four main material types. From these sorted materials, different subsamples were created according to each dataset's objective. The three datasets consist of cropped RGB line-scan camera images and mass measurements. Dataset 1 contains labeled single-particle images for the training of image classification, object detection, and segmentation networks and is organized by material type (ferrous, non-ferrous, printed circuit board, and plastic) and by particle size (12.5 mm - 25 mm and 25 mm - 50 mm). Dataset 2 follows the same structure and provides single-particle images together with per-object masses for the training of regression models for particle mass prediction. Dataset 3 provides single-particle images from three mixed samples with ground-truth compositions to validate models trained on Datasets 1 and 2. To serve as an additional validation dataset, Dataset 1 also includes the mass of each material fraction. These datasets enable the development and evaluation of models for the identification of material types in shredded WEEE, for particle mass prediction, and sensor-based material flow characterization, supporting improved process monitoring, quality control, and optimization of sorting processes.
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BibTeX
@article{vogelgesang2026material,
title = {{Material flow composition dataset: RGB-camera-based single-particle images and particle mass data of shredded WEEE}},
author = {Malte Vogelgesang and Alice Do Carmo Precci Lopes and Emanuel Ionescu and Liselotte Schebek},
journal = {Data in Brief},
year = {2026},
month = oct,
doi = {10.1016/j.dib.2025.112413},
url = {https://hal.science/hal-05789802v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Material flow composition dataset: RGB-camera-based single-particle images and particle mass data of shredded WEEE
AU - Malte Vogelgesang
AU - Alice Do Carmo Precci Lopes
AU - Emanuel Ionescu
AU - Liselotte Schebek
PY - 2026
DA - 2026/10/09
JO - Data in Brief
DO - 10.1016/j.dib.2025.112413
UR - https://hal.science/hal-05789802v1
AB - Mass-based material flow compositions (MFCOs) are among the most relevant indicators for the quality of sorting processes in waste management. They provide the basis for the assessment and improvement of the purity and yield of material streams. While MFCOs are currently primarily determined through labor-intensive manual sorting analyses, recent research has explored sensor-based material flow characterization (SBMC). Near-infrared spectroscopy (NIR) is effective for plastic waste but cannot reliably distinguish many material types in shredded waste electrical and electronic equipment (WEEE). Therefore, this article aims to provide data for research on SBMC with more versatile and often already implemented RGB cameras in sorting plants. The generated data contain shredded WEEE, which was recorded on a sensor-based sorting machine on an industrial scale. The samples were taken from an industrial WEEE recycling facility in Germany and manually presorted into four main material types. From these sorted materials, different subsamples were created according to each dataset's objective. The three datasets consist of cropped RGB line-scan camera images and mass measurements. Dataset 1 contains labeled single-particle images for the training of image classification, object detection, and segmentation networks and is organized by material type (ferrous, non-ferrous, printed circuit board, and plastic) and by particle size (12.5 mm - 25 mm and 25 mm - 50 mm). Dataset 2 follows the same structure and provides single-particle images together with per-object masses for the training of regression models for particle mass prediction. Dataset 3 provides single-particle images from three mixed samples with ground-truth compositions to validate models trained on Datasets 1 and 2. To serve as an additional validation dataset, Dataset 1 also includes the mass of each material fraction. These datasets enable the development and evaluation of models for the identification of material types in shredded WEEE, for particle mass prediction, and sensor-based material flow characterization, supporting improved process monitoring, quality control, and optimization of sorting processes.
SN - 2352-3409
ER -