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Transformer oil leakage detection with sampling-WIoU module

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Abstract

In order to detect the oil leakage problem of transformers in time and avoid the abnormal operation of power system caused by transformer oil leakage, this paper proposed a transformer oil leakage detection method based on improved YOLOv5. Firstly, by adding \(4\times\) sampling layer to the FPN structure, the feature information was fused across layers to improve the detection accuracy of the model. Secondly, the Wise-IoU (WIoU) bounding loss function with dynamic non-monotony focusing mechanism was introduced to accelerate the training and reasoning of the network, and the overall performance of the model was further improved by balancing the learning of low and high-quality samples. Finally, the model was improved from Anchor-based to Anchor-free, which greatly reduced the number of parameters and calculation amount of the model, and had better performance than the original model. Used the self-built outdoor transformer oil leakage data set for training and testing, the results showed that compared with the original model, the improved network precision was increased by 7.3%, the recall was increased by 5.0%, the mAP0.5 was increased by 6.4%, the number of parameters was decreased by 28.2%, and the reasoning speed increased by 51.7%, which was beneficial to engineering deployment.

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Data Availability

The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61801319, in part by Sichuan Science and Technology Program under Grant 2020JDJQ0061 and 2021YFG0099, in part by Innovation Fund of Chinese Universities under Grant 2020HYA04001, in part by the Sichuan University of Science and Engineering Talent Introduction Project under Grant 2020RC33, in part by the Postgraduate Innovation Fund Project of Sichuan University of Science and Engineering under Grant Y2022124.

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Author notes

  1. Chenghao Wang and Zhongqiang Luo have contributed equally to this work.

Authors and Affiliations

  1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China

    Chenghao Wang, Zhongqiang Luo & Ziyuan Qi

  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China

    Zhongqiang Luo

Authors
  1. Chenghao Wang
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  2. Zhongqiang Luo
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Contributions

Conceptualization was done by ZL; methodology was done by CW; investigation was done by CW and ZQ; writing–original draft preparation was done by CW and ZQ; writing–review and editing was done by ZL; supervision was done by ZL; project administration was done by ZL; and funding acquisition was done by ZL. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Zhongqiang Luo.

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Wang, C., Luo, Z. & Qi, Z. Transformer oil leakage detection with sampling-WIoU module. J Supercomput 80, 7349–7368 (2024). https://doi.org/10.1007/s11227-023-05748-5

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