Open AccessAccurate Positioning of the Substation Instruments in Images by Using a Method Based on Convolutional Neural Networks
Author(s) -
Chuanhua Yang,
Kun Huang,
Yujie Li,
Yue Tang,
Jianying Yuan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1288/1/012050
Subject(s) - convolutional neural network , computer science , artificial intelligence , pooling , convolution (computer science) , kernel (algebra) , pattern recognition (psychology) , matching (statistics) , feature (linguistics) , sensitivity (control systems) , artificial neural network , algorithm , mathematics , engineering , statistics , linguistics , philosophy , combinatorics , electronic engineering
Aiming at solving the problems of sensitivity to instrument shape and low accuracy of positioning by using the traditional feature matching points based methods, a novel positioning approach based on convolutional neural network is proposed in this work. The designed convolutional neural network consists of two convolutional layers, two pooling layers and two fully connected layers. The objective function adopts cross-entropy and the optimization method adopts Adam algorithm. We used 7000 images collected at different time and situations as test samples. We discussed the performance of the proposed algorithm under different amount of training data, different convolution kernel sizes and different number of convolution kernels in the experiments. Compared with the traditional feature matching point based method, the proposed method has higher recognition accuracy and lower false positioning rate.