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An improved insulator health monitoring method based on YOLOv5

Published: 31 July 2024 Publication History

Abstract

In order to ensure the safe and stable operation of transmission lines, aiming at the problems of damage and pollution flashover caused by long-term complex environmental influences of transmission line insulators, an improved detection method of transmission line insulator health status based on YOLOv5 combined with attention mechanism is proposed. In particular, CPAM and CBAM modules are incorporated into feature extraction and fusion stages respectively within the YOLOv5 algorithm structure, resulting in a significant improvement in model detection accuracy. Experimental results demonstrate that this improved approach enhances insulator state detection by 4.7% compared to the original YOLOv5, thus offering substantial practical value.

References

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Tan J. Automatic insulator detection for power line using aerial images powered by convolutional neural networks[C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1748(4): 042012.
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Liao G P, Yang G J, Tong W T, Study on power line insulator defect detection via improved faster region-based convolutional neural network[C]//2019 IEEE 7th international conference on computer science and network technology (ICCSNT). IEEE, 2019: 262-266.
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Lewis D, Kulkarni P. Insulator defect detection[J]. IEEE Dataport, 2021.

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  1. An improved insulator health monitoring method based on YOLOv5

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 July 2024

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