Automatic image-based waste classification

  1. Victoria Ruiz
  2. Ángel Sánchez
  3. José Vélez
  4. Bogdan Raducanu
Buch:
From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (coord.)

Verlag: Springer Suiza

ISBN: 978-3-030-19651-6

Datum der Publikation: 2019

Seiten: 422-431

Art: Buch-Kapitel

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Zusammenfassung

The management of solid waste in large urban environmentshas 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 wereobtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset.

Fuente de los datos: Dialnet