Résumé:
In recent years, advancements in waste sorting have been significantly enhanced by the integration of deep learning algorithms. In this regard, LusTra proposes a waste type recognition system using sound and accelerometer data. Two waste types are considered: Polyethylene Terephthalate (PET) bottles and Aluminium cans. Predictions through sound data, converted to Mel spectrograms, with Convolutional Neural Networks (CNN), are promising and result in an accuracy of 89% for PET waste and 90% for aluminium waste. Random forest with features extracted from the accelerometer data provide an accuracy of 76% and 86% for PET and aluminium respectively.
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