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Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

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Abstract

Insulators are important components of power transmission lines. Once a failure occurs, it may cause a large-scale blackout and other hidden dangers. Due to the large image size and complex background, detecting small defect objects is a challenge. We make improvements based on the two-stage network Faster R-convolutional neural networks (CNN). First, we use a hierarchical Swin Transformer with shifted windows as the feature extraction network, instead of ResNet, to extract more discriminative features, and then design the deformable receptive field block to encode global and local context information, which is utilized to capture key clues for detecting objects in complex backgrounds. Finally, the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model. As a result, the recall increases from 89.5% to 92.1%, and the average precision increases from 81.0% to 87.1%. To further prove the superiority of the proposed algorithm, we also tested the model on the public data set Pascal visual object classes (VOC), which also yields outstanding results.

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Acknowledgements

This work was supported by China Southern Power Grid Corporation Key Science and Technology Project: Research and Application of Key Technologies for Information Governance of the Smart Substations Secondary System (No. GZKJXM20191312).

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Authors and Affiliations

  1. Digital Grid Research Institute, China Southern Power Grid, Guangzhou, 510000, China

    Yu Xi, Bo Chen & Hao-Min Chen

  2. Institute of Electric Power Research of Guangxi Power Grid, Nanning, 530023, China

    Ke Zhou

  3. Institute of Electric Power Research of Guizhou Power Grid, Guiyang, 550000, China

    Ling-Wen Meng

  4. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Jing-Yi Zhang

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  1. Yu Xi
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Correspondence to Jing-Yi Zhang.

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Yu Xi received the B. Sc. and M. Sc. degrees in electrical engineering from South China University of Technology, China in 2014 and 2017, respectively. He is currently an engineer in Digital Grid Research Institute, China Southern Power Grid, China.

His research interests include smart grid and intelligent substation.

Ke Zhou received the B. Sc. degree in automation from Central South University, China in 2001, received the M. Sc. degree in control theory and control engineering from Central South University, China in 2004, and received the Ph. D. degree in electrical engineering from Hunan University, China in 2007. He is currently a professorate senior engineer in Electric Power Research Institute of Guangxi Power Grid Co., Ltd., China.

His research interests include smart grid and intelligent substation.

Ling-Wen Meng received the B. Sc. and M. Sc. degrees in electrical engineering from Shandong University, China in 2009 and 2012, respectively. She is currently a senior engineer in Institute of Electric Power Research of Guizhou Power Grid, China.

Her research interests include smart grid and intelligent substation.

Bo Chen received the B. Sc. and M. Sc. degrees in electrical engineering from Wuhan Electric Power University, China in 1992 and 1995, respectively, the Ph. D. degree from South China University of Technology, China in 2010. He is currently a professor level senior engineer in Digital Grid Research Institute, China Southern Power Grid, China.

His research interests include smart grid and intelligent substation.

Hao-Min Chen received the B. Sc. and M. Sc. degrees in electrical engineering from South China University of Technology, China in 2000 and 2003, respectively. He is currently a professor level senior engineer in Digital Grid Research Institute, China Southern Power Grid, China.

His research interests include smart grid and intelligent substation.

Jing-Yi Zhang received the B. Sc. degree in automation from School of Electronic Information, Jiangsu University of Science and Technology, China in 2016, and the M. Sc. degree in control engineering from School of Electrical and Information Engineering, Tianjin University, China in 2020. She is currently a Ph. D. degree candidate in control science and engineering at School of Electrical and Information Engineering, Tianjin University, China.

Her research interests include machine learning and data driven control.

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Xi, Y., Zhou, K., Meng, LW. et al. Transmission Line Insulator Defect Detection Based on Swin Transformer and Context. Mach. Intell. Res. 20, 729–740 (2023). https://doi.org/10.1007/s11633-022-1355-y

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