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Deep learning based insulator fault detection algorithm for power transmission lines

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Abstract

Aiming at the complex background of transmission lines at the present stage, which leads to the problem of low accuracy of insulator fault detection for small targets, a deep learning-based insulator fault detection algorithm for transmission lines is proposed. First, aerial images of insulators are collected using UAVs in different scenarios to establish insulator fault datasets. After that, in order to improve the detection efficiency of the target detection algorithm, certain improvements are made on the basis of the YOLOV9 algorithm. The improved algorithm enhances the feature extraction capability of the algorithm for insulator faults at a smaller computational cost by adding the GAM attention mechanism; at the same time, in order to realize the detection efficiency of small targets for insulator faults, the generalized efficient layer aggregation network (GELAN) module is improved and a new SC-GELAN module is proposed; the original loss function is replaced by the effective intersection-over-union (EIOU) loss function to minimize the difference between the aspect ratio of the predicted frame and the real frame, thereby accelerating the convergence speed of the model. Finally, the proposed algorithm is trained and tested with other target detection algorithms on the established insulator fault dataset. The experimental results and analysis show that the algorithm in this paper ensures a certain detection speed, while the algorithmic model has a higher detection accuracy, which is more suitable for UAV fault detection of insulators on transmission lines.

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The data that support the findings of this study are available on request from the corresponding author upon reasonable request.

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Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (U1806201), the Shandong Province Natural Science Foundation of China (ZR2022ME194) and the Major Basic Research Project of Shandong Province Natural Science Foundation (ZR2021ZD12).

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Contributions

H. W.; Data analysis and Writing, Conceptualization. Q. Y.; Conceptualization, Resources. B. Z.; Validation, writing-review and editing. D. G.; Methodology, Visualization. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Qing Yang.

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Wang, H., Yang, Q., Zhang, B. et al. Deep learning based insulator fault detection algorithm for power transmission lines. J Real-Time Image Proc 21, 115 (2024). https://doi.org/10.1007/s11554-024-01495-9

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