Classification of PS and ABS Black Plastics for WEEE Recycling Applications
This work addresses efficient recycling for sustainability by enabling automated sorting of specific plastics in WEEE, though it is incremental as it applies an existing CNN method to a new dataset.
The paper tackled the problem of classifying black plastics (PS and ABS) from electronic waste using image analysis, achieving 95% validation accuracy and 86.6% test accuracy with ABS correctly classified 100% of the time.
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.