@InProceedings{VictoriaRuiz2019,
author="Victoria Ruiz
and Angel Sanchez
and Jose F. Velez
and Bogdan Raducanu",
title="Automatic Image-Based Waste Classification",
booktitle="International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning",
year="2019",
volume="11487",
pages="422--431",
optkeywords="Computer Vision",
optkeywords="Deep learning",
optkeywords="Convolutional neural networks",
optkeywords="Waste classification",
abstract="The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6\% of accuracy. These are the best results obtained with the considered dataset.",
optnote="LAMP; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3273), last updated on Wed, 12 May 2021 12:52:26 +0200",
opturl="https://link.springer.com/chapter/10.1007/978-3-030-19651-6_41",
file=":http://refbase.cvc.uab.es/files/RSV2019.pdf:PDF"
}