MATEC Web Conf.
Volume 139, 20172017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|Number of page(s)||4|
|Published online||05 December 2017|
Insulator recognition based on convolution neural network
1 Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, 300387, Tianjin, China
* Corresponding author: firstname.lastname@example.org
Insulator fault detection plays an important role in maintaining the safety of transmission lines. Insulator recognition is a prerequisite for its fault detection. An insulator recognition algorithm based on convolution neural network (CNN) is proposed. A dataset is established to train the constructed CNN. The correct rate is 98.52% for 1220 training samples and the accuracy rate of testing is 89.04% on 1305 samples. The classification result of the CNN is further used to segment the insulator image. The test results show that the proposed method can realize the effective segmentation of insulators.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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