Assessing MobileNetV3 for plastic waste segregation

Abstract

To improve effective waste management practices at an individual level, ease of access to information is crucial within the South African context due to the informal manner in which it handles its plastic waste. The informal manner in which South Africa handles its plastic waste is linked to the lack of information that is accessible and reliable for public use (Jansen & Roberts, 2018). By addressing this issue, plastic waste recycling can be facilitated within households by providing individuals with information that is easily accessible. Convolutional Neural Networks (CNNs) can be used as a tool to improve effective individual plastic waste management by improving available information for public use to empower individual rights on plastic waste management practices. This will result in an increase in individual recycling behaviour at an individual level. Waste needs to be classified into different waste types due to the different methods of disposal that are required for each waste type. In terms of plastic waste, it can be recycled and reused as raw materials for production. However, the reprocessing of one type of plastic is not the same as the reprocessing of another type of plastic. A waste classification system is needed to improve access to information that is provided to the public, which is limited to the recycling of plastic waste. Thus, this research project aims to evaluate the suitability of using MobileNetV3 to run a waste classification AI. A model of MobileNetV3-Small was used as the architecture for the neural network because it is the latest architectural framework within the MobileNet family. The model was built and trained on the Keras Tensorflow open-source library and was developed for the Wadaba dataset, which is available at: https://wadaba.pcz.pl/. The MobileNetV3-Small architecture was tested at three different learning rates 0.0001, 0.001, and 0.01 to determine at which learning rate the model performs the best at recognizing the Wadaba dataset. It was found that at a learning rate of 0.0001, the model achieved an accuracy score of 59% with a validation loss of 1.21. At a learning rate of 0.001, the model achieved an accuracy score of 61% with a validation loss of 1.21. At a learning rate of 0.01, the model achieved an accuracy score of 61% with a validation loss of 1.24. The significance of the findings is that the model can recognize the Wadabba dataset with an average accuracy of 60% across the three learning rates, suggesting that the model can be used in applications that involve plastic waste classification.