Dual-branch CNN for the identification of recyclable materialsDual-branch CNN for the identification of recyclable materials Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2023-05-112021enThis research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK- 03110, ANASA).The classification of recyclable materials, and in particular the recovery of plastic, plays an important role in the economy, but also in environmental sustainability. This study presents a novel image classification model that can be efficiently used to distinguish recyclable materials. Building on recent work in deep learning and waste classification, we introduce the so-called “Dual-branch Multi-output CNN”, a custom convolutional neural network composed of two branches aimed to i) classify recyclables and ii) distinguish the type of plastic. The proposed architecture is composed of two classifiers trained on two different datasets, so as to encode complementary attributes of the recyclable materials. In our work, the Densenet121, ResNet50 and VGG16 architectures were used on the Trashnet dataset, along with data augmentation techniques, as well as on the WaDaBa dataset with physical variation techniques. In particular, our approach makes use of the joint utilization of the datasets, allowing the learning of disjoint label combinations. Our experiments confirm its effectiveness in the classification of waste material.http://creativecommons.org/licenses/by/4.0/2021 IEEE International Conference on Imaging Systems and Techniques Vogiatzis Antonios Βογιατζης Αντωνιος Chalkiadakis Georgios Χαλκιαδακης Γεωργιος Moirogiorgou Konstantia Μοιρογιωργου Κωνσταντια Livanos Georgios Λιβανος Γεωργιος Papadogiorgaki Maria Παπαδογιωργακη Μαρια Zervakis Michail Ζερβακης Μιχαηλ Institute of Electrical and Electronics Engineers Image classification Supervised classification Machine learning