  Pollution and climate change are some of the biggest challenges that humanity
is facing. In such a context, efficient recycling is a crucial tool for a
sustainable future. This work is aimed at creating a system that can classify
different types of plastics by using picture analysis, in particular, black
plastics of the type Polystyrene (PS) and Acrylonitrile Butadiene Styrene
(ABS). They are two common plastics from Waste from Electrical and Electronic
Equipment (WEEE). For this purpose, a Convolutional Neural Network has been
tested and retrained, obtaining a validation accuracy of 95%. Using a separate
test set, average accuracy goes down to 86.6%, but a further look at the
results shows that the ABS type is correctly classified 100% of the time, so it
is the PS type that accumulates all the errors. Overall, this demonstrates the
feasibility of classifying black plastics using CNN machine learning
techniques. It is believed that if a more diverse and extensive image dataset
becomes available, a system with higher reliability that generalizes well could
be developed using the proposed methodology.
