An Inspection System for Multi-Label Polymer Classification

Waste treatment, especially treatment of plastic waste, is arguably one of the biggest challenges that humanity faces in context of preserving the environment besides global warming. This work presents a visual inspection system for plastic classification and proposes a classification algorithm that is based on near-infrared spectroscopy and convolutional neural networks. The method allows for a highly accurate classification of several main polymer types while being robust against image disturbances occurring in a real world scenario. Most importantly, it is able to cope with layers of multiple materials. This work therefore offers for the very first time a solution to multi-material classification in the context of plastic recycling. Since the manual creation and annotation of layered materials is a cumbersome task due to the manifold of possible combinations, it is also shown how the creation of artificial data can greatly facilitate the ground truth generation.