TY  - CONF
AU  - Victoria Ruiz
AU  - Angel Sanchez
AU  - Jose F. Velez
AU  - Bogdan Raducanu
A2  - IWINAC
PY  - 2019//
TI  - Automatic Image-Based Waste Classification
T2  - LNCS
BT  - International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning
SP  - 422–431
VL  - 11487
KW  - Computer Vision
KW  - Deep learning
KW  - Convolutional neural networks
KW  - Waste classification
N2  - 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.
UR  - https://link.springer.com/chapter/10.1007/978-3-030-19651-6_41
L1  - http://refbase.cvc.uab.es/files/RSV2019.pdf
N1  - LAMP; 600.120
ID  - Victoria Ruiz2019
ER  -