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Dual-branch CNN for the identification of recyclable materials

Vogiatzis Antonios, Chalkiadakis Georgios, Moirogiorgou Konstantia, Livanos Georgios, Papadogiorgaki Maria, Zervakis Michail

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/E136593A-B0F5-4CC2-A6C1-09435791646B-
Αναγνωριστικόhttps://doi.org/10.1109/IST50367.2021.9651347-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9651347-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςDual-branch CNN for the identification of recyclable materialsen
ΔημιουργόςVogiatzis Antoniosen
ΔημιουργόςΒογιατζης Αντωνιοςel
ΔημιουργόςChalkiadakis Georgiosen
ΔημιουργόςΧαλκιαδακης Γεωργιοςel
ΔημιουργόςMoirogiorgou Konstantiaen
ΔημιουργόςΜοιρογιωργου Κωνσταντιαel
ΔημιουργόςLivanos Georgiosen
ΔημιουργόςΛιβανος Γεωργιοςel
ΔημιουργόςPapadogiorgaki Mariaen
ΔημιουργόςΠαπαδογιωργακη Μαριαel
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηThe 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.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-05-11-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαImage classificationen
Θεματική ΚατηγορίαSupervised classificationen
Θεματική ΚατηγορίαMachine learningen
Βιβλιογραφική ΑναφοράA. Vogiatzis, G. Chalkiadakis, K. Moirogiorgou, G. Livanos, M. Papadogiorgaki and M. Zervakis, "Dual-branch CNN for the identification of recyclable materials," presented at the 2021 IEEE International Conference on Imaging Systems and Techniques (IST), Kaohsiung, Taiwan, 2021, doi: 10.1109/IST50367.2021.9651347.en

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