2023-09-28
Detecting Temperature Anomaly at the Key Parts of Power Transmission and Transformation Equipment Using Infrared Imaging Based on Segformer
By
Progress In Electromagnetics Research M, Vol. 119, 117-128, 2023
Abstract
Methods of manual analysis for infrared image and temperature detection of power transmission and transformation equipment typically have problems, such as low efficiency, strong subjectivity, easy to make mistakes and poor real-time feedback. In this paper, a high temperature anomaly detection method based on SegFormer in infrared image of power transmission and transformation equipment is proposed. Many infrared images of power transmission and transformation equipment are collected and preprocessed, and the temperature information of each infrared image is read out using the DJI sdk tool to construct the temperature data matrix. In the segmentation stage, the SegFormer network is used to segment the key parts of the power transmission and transformation equipment to obtain the mask for detection. The maximum values of the temperature data in the mask area are calculated, and the high temperature anomaly detection atthe key parts of the power transmission and transformation equipment is realized. The test results on the test set show that the overall performance of the method is the highest as compared to other methods such as FCN, UNet, SegNet, DeepLabV3+, and an automatic temperature recognition can be realized, which has important practical value for the detection of high temperature anomaly at the key parts of power transmission and transformation equipment.
Citation
Haozhe Wang, Dawei Gong, Guokai Cheng, Jiong Jiang, Dun Wu, Xinhua Zhu, Shengnan Wu, Gaoao Ye, Lingling Guo, and Sailing He, "Detecting Temperature Anomaly at the Key Parts of Power Transmission and Transformation Equipment Using Infrared Imaging Based on Segformer," Progress In Electromagnetics Research M, Vol. 119, 117-128, 2023.
doi:10.2528/PIERM23081104
References

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        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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        Google Scholar

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.        Google Scholar