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

• 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: Neural networks (Computer science); Transfer learning (Machine learning); Passive components—Classification; Reverse engineering; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3025
• Institution: De La Salle University

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.