Classification of Hand-Drawn Basic Circuit Components Using Convolutional Neural Networks

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dc.authoridKoseoglu, Murat/0000-0003-3774-1083
dc.authorwosidKoseoglu, Murat/ABG-8975-2020
dc.contributor.authorGunay, Mihriban
dc.contributor.authorKoseoglu, Murat
dc.contributor.authorYildirim, Ozal
dc.date.accessioned2024-08-04T20:48:48Z
dc.date.available2024-08-04T20:48:48Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) -- JUN 26-27, 2020 -- TURKEYen_US
dc.description.abstractIn this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.en_US
dc.description.sponsorshipIEEE Turkey Secten_US
dc.identifier.doi10.1109/hora49412.2020.9152866
dc.identifier.endpage138en_US
dc.identifier.isbn978-1-7281-9352-6
dc.identifier.scopus2-s2.0-85089699965en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage134en_US
dc.identifier.urihttps://doi.org/10.1109/hora49412.2020.9152866
dc.identifier.urihttps://hdl.handle.net/11616/99470
dc.identifier.wosWOS:000644404300023en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (Hora 2020)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectclassificationen_US
dc.subjectCNNen_US
dc.subjectcircuit componentsen_US
dc.titleClassification of Hand-Drawn Basic Circuit Components Using Convolutional Neural Networksen_US
dc.typeConference Objecten_US

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