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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/E136593A-B0F5-4CC2-A6C1-09435791646B | ||
| Year | 2021 | ||
| Type of Item | Conference Full Paper | ||
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| Bibliographic Citation | A. 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. https://doi.org/10.1109/IST50367.2021.9651347 | ||
| Appears in Collections |
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.
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