Open AccessA Novel Electronic Component Classification Algorithm Based on Hierarchical Convolution Neural Network
Author(s) -
Xiaomei Hu,
Jun Xu,
Jun Wu
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/474/5/052081
Subject(s) - convolutional neural network , computer science , artificial intelligence , pattern recognition (psychology) , feature extraction , deep learning , classifier (uml) , convolution (computer science) , contextual image classification , pooling , artificial neural network , feature (linguistics) , component (thermodynamics) , coding (social sciences) , algorithm , image (mathematics) , mathematics , physics , thermodynamics , linguistics , philosophy , statistics
In the paper, the author proposes a recognition and classification algorithm of electronic components based on hierarchical convolutional neural network (NH-CNN) that reduces the computational complexity on the drawbacks of traditional image classification method based on deep learning such as the failure of effectively combining multiple deep characteristics, the poor performance of classifier, the difficulty of parameter adjustment and the long training time. The algorithm is trained through the Convolutional Automatic Coding (CAE) layer to obtain relevant feature maps, which reduces the input parameters of the designed convolutional neural network. Since the CAE model and the convolutional neural network have similar convolution and pooling operations, the feature maps obtained from the CAE model are put into the designed neural network based on the transfer learning method. Finally, the feature fusion method is used to output the obtained features to the fully connected layer, which is used to better express the depth information contained in the electronic component images and improve the accuracy of classification. The experimental results show that the proposed algorithm can effectively extract depth features with high precision of 94.26% and less complexity and overcome the defects of traditional image classification algorithms such as manual image extraction and low classification efficiency.