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Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks; Deep Learning

Authors: Christian Linder ; Frank Gaibler ; Andreas Margraf and Steffen Geinitz

Affiliation: Fraunhofer Institute for Casting, Composite and Processing Technology IGCV, Am Technologiezentrum 2, 86159 Augsburg, Germany

Keyword(s): Signal Processing, Deep Learning, Tracer-Based-Sorting, Synthetic Data, Convolutional Neural Network (CNN), Fluorescent Tracers, Data Augmentation, Recycling, Plastics Sorting.

Abstract: Increasing environmental awareness and new regulations require an improvement of the waste cycle of plastic packaging. Tracer-Based-Sorting (TBS) technology can meet these challenges. Previous studies show the market potential of the technology. This work improves on the solution approach using artificial intelligence to maximize the number of tracers that can be detected accurately. A convolutional neural network and random forest classifier are compared for classification of each tracer based on signal intensities. The approach is validated on different settings using synthetic data to counter the low amount of available data. The results show that theoretically up to 120 tracers can be classified simultaneously under near-optimal conditions. Under more difficult conditions, the maximum number of tracers is reduced to 45. Thus, the approach can increase the diversity of TBS by increasing the maximum tracer count and enable a broader range of applications. This helps to establish th e technology in the field of recycling. (More)

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Paper citation in several formats:
Linder, C.; Gaibler, F.; Margraf, A. and Geinitz, S. (2022). Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 323-330. DOI: 10.5220/0011337000003332

@conference{ncta22,
author={Christian Linder. and Frank Gaibler. and Andreas Margraf. and Steffen Geinitz.},
title={Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
year={2022},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011337000003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA
TI - Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data
SN - 978-989-758-611-8
IS - 2184-3236
AU - Linder, C.
AU - Gaibler, F.
AU - Margraf, A.
AU - Geinitz, S.
PY - 2022
SP - 323
EP - 330
DO - 10.5220/0011337000003332
PB - SciTePress