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.
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Acknowledgements
The authors gratefully acknowledge the financial support of the CYTED Network “Ibero-American Thematic Network on ICT Applications for Smart Cities” (Ref: 518RT0559) and also the Spanish MICINN RTI Project (Ref: RTI2018-098019-B-100).
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Ruiz, V., Sánchez, Á., Vélez, J.F., Raducanu, B. (2019). Automatic Image-Based Waste Classification. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_41
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