| Format | |
|---|---|
| BibTeX | |
| MARCXML | |
| TextMARC | |
| MARC | |
| DublinCore | |
| EndNote | |
| NLM | |
| RefWorks | |
| RIS |
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.