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Pollution and climate change are some of the biggest challenges that humanityis facing. In such a context, efficient recycling is a crucial tool for asustainable future. This work is aimed at creating a system that can classifydifferent types of plastics by using picture analysis, in particular, blackplastics of the type Polystyrene (PS) and Acrylonitrile Butadiene Styrene(ABS). They are two common plastics from Waste from Electrical and ElectronicEquipment (WEEE). For this purpose, a Convolutional Neural Network has beentested and retrained, obtaining a validation accuracy of 95%. Using a separatetest set, average accuracy goes down to 86.6%, but a further look at theresults shows that the ABS type is correctly classified 100% of the time, so itis the PS type that accumulates all the errors. Overall, this demonstrates thefeasibility of classifying black plastics using CNN machine learningtechniques. It is believed that if a more diverse and extensive image datasetbecomes available, a system with higher reliability that generalizes well couldbe developed using the proposed methodology.

Classification of PS and ABS Black Plastics for WEEE Recycling Applications