Defect diagnosis technology of typical components on transmission line based on Fully Convolutional Network
This paper presents an intelligent defect diagnosis algorithm based on fully convolutional neural network for typical transmission line components. Based on the region-based fully convolutional neural network algorithm and combined with deformable convolution, feature context fusion, clustering, the...
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| Veröffentlicht in: | Journal of physics. Conference series 2020-01, Vol.1453 (1), p.12108 |
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| container_title | Journal of physics. Conference series |
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| creator | Zhenyu, LI Wanguo, Wang Tao, LI Guangxiu, LIU Zengwei, LI Yuan, Tian |
| description | This paper presents an intelligent defect diagnosis algorithm based on fully convolutional neural network for typical transmission line components. Based on the region-based fully convolutional neural network algorithm and combined with deformable convolution, feature context fusion, clustering, the feature expression ability of neural network is improved. Through the improvement, the algorithm adapts to the object deformation and scale difference. In addition, the training effect is improved by improving the sample labeling strategy. In this paper, the defect diagnosis of four kinds of transmission line components(insulator, vibration damper, grading ring, wire clamp) is realized. |
| doi_str_mv | 10.1088/1742-6596/1453/1/012108 |
| format | Article |
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| subjects | Algorithms Artificial neural networks Clustering Deformation effects Diagnosis Formability Neural networks Physics Transmission lines Vibration isolators |
| title | Defect diagnosis technology of typical components on transmission line based on Fully Convolutional Network |
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