DeepTronic: An electronic device classification model using deep convolutional neural networks

This paper presents a novel and straightforward way of classifying discrete and surface-mount electronic components found on electronic prototypes using transfer learning and deep convolutional neural networks (DCNN). The goal of this study is to precisely classify images of electronic components in...

Full description

Saved in:
Bibliographic Details
Main Authors: Salvador, Rodolfo C., Bandala, Argel A., Javel, Irister M., Bedruz, Rhen Anjerome R., Dadios, Elmer P., Vicerra, Ryan Rhay P.
Format: text
Published: Animo Repository 2019
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3025
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-4024
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-40242021-11-22T00:29:33Z DeepTronic: An electronic device classification model using deep convolutional neural networks Salvador, Rodolfo C. Bandala, Argel A. Javel, Irister M. Bedruz, Rhen Anjerome R. Dadios, Elmer P. Vicerra, Ryan Rhay P. This paper presents a novel and straightforward way of classifying discrete and surface-mount electronic components found on electronic prototypes using transfer learning and deep convolutional neural networks (DCNN). The goal of this study is to precisely classify images of electronic components into six classes: resistor, capacitor, inductor, transformer, diode, or integrated circuit. Each class of electronic components has over 100 images which are augmented and preprocessed to match the input layer requirements of the deep learning models used. The dataset was divided into a ratio of 70:30, where 70% was used for training and 30% was used for testing and validation. Transfer Learning (TL) was done using three pre-trained deep learning models that are available on MATLAB's Neural Network Toolbox: Inception-v3, GoogleNet, and Resnet101. Using this approach provides faster deployment and only requires fewer lines of coding compared to typical deep learning classification methods which make use of Python, Tensorflow, and Keras. The results of the experiment showed that Inception-v3 has the highest validation accuracy of 94.64% in classifying electronic components. © 2018 IEEE. 2019-03-12T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/3025 Faculty Research Work Animo Repository Neural networks (Computer science) Transfer learning (Machine learning) Passive components—Classification Reverse engineering Manufacturing
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Neural networks (Computer science)
Transfer learning (Machine learning)
Passive components—Classification
Reverse engineering
Manufacturing
spellingShingle Neural networks (Computer science)
Transfer learning (Machine learning)
Passive components—Classification
Reverse engineering
Manufacturing
Salvador, Rodolfo C.
Bandala, Argel A.
Javel, Irister M.
Bedruz, Rhen Anjerome R.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
DeepTronic: An electronic device classification model using deep convolutional neural networks
description This paper presents a novel and straightforward way of classifying discrete and surface-mount electronic components found on electronic prototypes using transfer learning and deep convolutional neural networks (DCNN). The goal of this study is to precisely classify images of electronic components into six classes: resistor, capacitor, inductor, transformer, diode, or integrated circuit. Each class of electronic components has over 100 images which are augmented and preprocessed to match the input layer requirements of the deep learning models used. The dataset was divided into a ratio of 70:30, where 70% was used for training and 30% was used for testing and validation. Transfer Learning (TL) was done using three pre-trained deep learning models that are available on MATLAB's Neural Network Toolbox: Inception-v3, GoogleNet, and Resnet101. Using this approach provides faster deployment and only requires fewer lines of coding compared to typical deep learning classification methods which make use of Python, Tensorflow, and Keras. The results of the experiment showed that Inception-v3 has the highest validation accuracy of 94.64% in classifying electronic components. © 2018 IEEE.
format text
author Salvador, Rodolfo C.
Bandala, Argel A.
Javel, Irister M.
Bedruz, Rhen Anjerome R.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
author_facet Salvador, Rodolfo C.
Bandala, Argel A.
Javel, Irister M.
Bedruz, Rhen Anjerome R.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
author_sort Salvador, Rodolfo C.
title DeepTronic: An electronic device classification model using deep convolutional neural networks
title_short DeepTronic: An electronic device classification model using deep convolutional neural networks
title_full DeepTronic: An electronic device classification model using deep convolutional neural networks
title_fullStr DeepTronic: An electronic device classification model using deep convolutional neural networks
title_full_unstemmed DeepTronic: An electronic device classification model using deep convolutional neural networks
title_sort deeptronic: an electronic device classification model using deep convolutional neural networks
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/3025
_version_ 1718383341021954048