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.
CITATION STYLE
Ruiz, V., Sánchez, Á., Vélez, J. F., & Raducanu, B. (2019). Automatic Image-Based Waste Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11487 LNCS, pp. 422–431). Springer Verlag. https://doi.org/10.1007/978-3-030-19651-6_41
Mendeley helps you to discover research relevant for your work.