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An evaluation of plastic classification techniques using Hyperspectral Near-Infrared Imaging

Due to the slow biodegradation of plastic and the increasing amount of produced material, recycling of plastic waste is of major importance to reduce the negative impact on the environment that an ever-growing world population causes. The versatile nature of plastic products, however, impede the development of simple recycling technologies and due to the low cost of virgin material as well as the reduced quality of recyclate, the incentives for recycling are limited from an economic point of view.

This work provides an evaluation of hyperspectral image-based classification techniques. All considered classification techniques can be separated into two groups, namely neural networks and non-neural networks. The latter group can also be seen as the more conventional approach and comprises the quotient-based classifier  and the spectral angle mapper (SAM). Neural networks on the other side originate from the field of machine learning and are conceptually motivated by the human brain. Their goal is to automatically learn and recognize important relationships or patterns in data. Most notably, not only the classification of pure spectra was evaluated but also the task of multi-material detection in mixed spectra was considered.

Copyright:	© ANTS - Institut für anthropogene Stoffkreisläufe an der RWTH Aachen
Quelle:	SBSC 2018 (März 2018)
Seiten:	7
Preis:	€ 3,50
Autor:	Dipl. Ing. Marcel Bosling Tarek Stiebel Prof. Dr.-Ing. Thomas Pretz Aljoscha Steffens Univ.-Prof. Prof. Dr.-Ing Dorit Merhof
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