Detection method based on improved faster R-CNN for pin defect in transmission lines

Jun Wu; Sheng Cheng; Shangzhi Pan; Wei Xin; Liangjun Bai; Liang Fan; Xiaohu Dong

doi:10.1051/e3sconf/202130001011

All issues

Volume 300 (2021)

E3S Web Conf., 300 (2021) 01011

References

Open Access

Issue		E3S Web Conf. Volume 300, 2021 2021 2^nd International Conference on Energy, Power and Environmental System Engineering (ICEPESE2021)


Article Number		01011
Number of page(s)		6
Section		Energy and Power Engineering
DOI		https://doi.org/10.1051/e3sconf/202130001011
Published online		06 August 2021

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[1] M. Z. Fortes, V. H. Ferreira and R. Zanghi, “Fault Diagnosis in Transmission Lines: Trends and Main Research Areas, ” IEEE Latin Am. Trans., 13, 9 (2015). [Google Scholar]

[2] H.M. La, T.H. Dinh, N.H. Pham, Q.P. Ha, A.Q. Pham, “ Automated robotic monitoring and inspection of steel structures and bridges”, Robotica, 37, 21 (2019). [Google Scholar]

[3] Y. Yang, Y. Wang, and H. Jiao, “Insulator identification and self-shattering detection based on mask region with convolutional neural network, ” J. Electron. Imaging, 28, 10 (2019). [Google Scholar]

[4] X. Xiang, N. Lv, X. Guo, S. Wang, Abdulmotaleb El Saddik, “Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance”, Sensors, 18, 9 (2018) [Google Scholar]

[5] Y. Wang, Q. Li, and B. Chen. “Image classification towards transmission line fault detection via learning deep quality-aware fine-grained categorization, ” J. Vis. Commun. Image Represent., 64. 6, (2019). [Google Scholar]

[6] K. He, X. Zhang, S. Ren and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition, ” IEEE Trans. Pattern Anal. Mach. Intell., 37, 13 (2015). [PubMed] [Google Scholar]

[7] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, Laviolette Franois, M. Marchand, V. Lempitsky. “Domain-Adversarial Training of Neural Networks, ” J. Mach. Learn. Res., 17, 35 (2017). [Google Scholar]