Resumen de Automatic image-based waste classification

Victoria Ruiz de Velasco, Ángel José Sánchez Navarro, José Vélez, Bogdan Raducanu

• 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.