State Grid Zhejiang Electric Power Co., LTD., Ningbo Power Supply Company
China
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State Grid Zhejiang Electric Power Co., LTD., Ningbo Power Supply Company
China
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1. Wang, T., W. Liu, J. Zhao, et al. "A rough set-based bio-inspired fault diagnosis method for electrical substations," International Journal of Electrical Power and Energy Systems, Vol. 119, 105961, 2020.
doi:10.1016/j.ijepes.2020.105961
2. Li, Y. J., H. T. Li, S. Q. Song, et al. "Research on temperature detection of internal conductor in GIS basing on infrared thermal imaging," Electric Power Engineering Technology, 142-146, 2019.
3. Chen, F., J. G. Yao, Z. S. Li, et al. "The method to extract shed surface image of a single insulator from infrared image of a insulator string," Power System Technology, 220-224, 2010.
4. Tang, Q. J., J. Y. Liu, Y. Wang, et al. "Infrared image edge recognition and defect quantitative determination based on the algorithm of fuzzy C-means clustering and canny operator," Infrared and Laser Engineering, 281-285, 2016.
5. Cui, J. Y., Y. D. Cao, and W. J. Wang, "Application of an improved algorithm based on watershed combined with Krawtchouk invariant moment in inspection image processing of substations," Proceedings of the CSEE, Vol. 35, No. 6, 1329-1335, 2015.
6. Gong, D., T. Ma, J. Evans, and S. He, "Deep neural networks for image super-resolution in optical microscopy by using modified hybrid task cascade U-Net," Progress In Electromagnetics Research, Vol. 171, 185-199, 2021.
doi:10.2528/PIER21110904
7. Zhang, X., W. Lin, M. Xiao, and H. Ji, "Multimodal 2.5D convolutional neural network for diagnosis of Alzheimer's disease with magnetic resonance imaging and positron emission tomography," Progress In Electromagnetics Research, Vol. 171, 21-34, 2021.
doi:10.2528/PIER21051102
8. Hou, C. P., H. G. Zhang, W. Zhang, et al. "Identification method for spontaneous explosion defects of transmissionline insulators," Electrical Power System Automatic, Vol. 31, No. 6, 1-6, 2019.
9. Sampedro, C., J. Rodriguez-Vazquez, A. Rodriguez-Ramos, et al. "Deep learning-based system for automatic recognition and diagnosis of electrical insulator strings," IEEE Access, Vol. 7, 101283-101308, 2019.
doi:10.1109/ACCESS.2019.2931144
10. Wang, B., M. Dong, M. Ren, et al. "Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis," IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 8, 5345-5355, 2020.
doi:10.1109/TIM.2020.2965635
11. Vaswani, A., N. Shazeer, N. Parmar, et al. "Attention is all you need," Proceedings of the International Conference on Neural Information Processing Systems, 6000-6010, 2017.
12. Han, K., Y. Wang, H. Chen, et al. "A survey on vision transformer," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 1, 87-110, 2023.
doi:10.1109/TPAMI.2022.3152247
13. Dosovitskiy, A., L. Beyer, A. Kolesnikov, et al., "An image is worth 16 x 16 words: Transformers for image recognition at scale,", [EB/OL], 2020, https://arxiv.org /abs/2010.11929.
14. Zheng, S., J. Lu, H. Zhao, et al. "Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6881-6890, 2021.
15. Xie, E., W. Wang, Z. Yu, et al. "SegFormer: Simple and efficient design for semantic segmentation with transformers," Proceedings of the International Conference on Neural Information Processing Systems, 2021.
16. Wang, X. J., Z. N. Zheng, Y. C. Fang, et al. "Defect diagnosis method for composite insulators based on U-net segmentation,", Fujian Province: CN114037694A, Feb. 11, 2022.
17. Long, J., E. Shelhamer, T. Darrell, et al. "Fully convolutional networks for semantic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440, 2015.
18. Ronneberger, O., P. Fischer, and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation," Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 234-241, 2015.
19. Badrinarayanan, V., A. Kendall, and R. Cioplla, "SegNet: A deep convolutional encoder-decoder architecture for image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, 2481-2495, 2017.
doi:10.1109/TPAMI.2016.2644615
20. Chen, L. C., Y. Zhu, G. Papandreou, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation," Proceedings of the European Conference on Computer Vision, 801-818, 2018.
