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An intelligent recognition system for insulator string defects based on dimension correction and optimized faster R-CNN

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Abstract

In this paper, an intelligent recognition system for insulator based on dimension correction and optimized faster region with convolutional neural network (R-CNN) is proposed. In the process of insulator pictures shooting, a laser radar is used to calculate the UAV correction vector. The position of the UAV is adjusted to ensure the consistency of the spatial dimensions of the pictures taken in different time dimensions. Based on the almost invariant spatial dimension, the faster R-CNN image recognition algorithm is optimized. When the target detection frame is generated, marked reference pictures are added to narrow the search range, improve the target detection frame generation speed, and reduce the number of pictures during training. Experiments and comparison analysis are included. They verify the optimized faster R-CNN image recognition algorithm requires less pictures and recognition time, and the recognition accuracy increased from 85.6 to 97.3%.

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Acknowledgements

The authors acknowledge the financial support from China Southern Power Grid Company Limited.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072, Hubei, China

    Tao Lin & Xiaowei Liu

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  1. Tao Lin
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  2. Xiaowei Liu
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Correspondence to Xiaowei Liu.

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Lin, T., Liu, X. An intelligent recognition system for insulator string defects based on dimension correction and optimized faster R-CNN. Electr Eng 103, 541–549 (2021). https://doi.org/10.1007/s00202-020-01099-z

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  • DOI: https://doi.org/10.1007/s00202-020-01099-z

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