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Insulator defect detection in complex scenarios based on cascaded networks with lightweight attention mechanism

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Abstract

The power system stands as a crucial infrastructure pivotal to the country’s modern economic, security and social development. This paper addresses challenges in insulator fault detection on power transmission towers, leveraging the advancements in unmanned aerial vehicles equipped with target detection methods. We propose a novel method for insulator defect detection based on YOLOv5 (You Only Look Once), aiming to mitigate the issues associated with high missed detection rates. Small insulator faults and the limitation of unmanned aerial vehicle on-board capacity make it difficult to detect comprehensively. Firstly, the cluster analysis was carried out on the training data to obtain 9 kinds of better preset anchors for insulator detection, which improved the accuracy of the model to identify the location of targets. Secondly, the base-model is used to detect the insulator region, and the detection results are input into the sub-model to detect the location of faults, so as to form a cascade model, and make full use of the advantages of the two models to solve the problem of high missed detection rate. Finally, a lightweight attention module combining channel attention module and spatial attention module is added in YOLOv5 to improve the base-model’s attention to insulator region and suppress complex background features. Experimental results show that compared with the original model, the average precision of the proposed method for insulator detection is increased by 6.9%, and the missed detection rate of the fault location is 30% lower. Significant improvements in insulator detection performance have been achieved using the method proposed in this paper. It can not only effectively improve the detection accuracy, but also make the missed detection rate lower to meet the requirements of insulator defect detection and fault warning applications in complex environments, which proves that it has a wide range of application prospects in practice, especially in the field of power industry.

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Data Availability

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

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Funding

This research was funded by the National Key Research and Development Program (2022YFB2804402).

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Authors and Affiliations

  1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

    Yang Ning & Li Xiang

  2. College of Robotics, Beijing Union University, Beijing, 100101, China

    Yang Ning, Li Xiang, Jing Hongyuan, Shang Xinna & Chen Aidong

  3. Research Centre for Multi-Intelligent Systems, Beijing Union University, Beijing, 100101, China

    Jing Hongyuan, Shang Xinna & Chen Aidong

  4. Computer and Information Technology School, Zhejiang Changzheng Vocational and Technical College, Hangzhou, 310023, China

    Shen Ping

Authors
  1. Yang Ning
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  2. Li Xiang
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  3. Jing Hongyuan
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  4. Shang Xinna
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  5. Shen Ping
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  6. Chen Aidong
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Contributions

Conceptualization, N.Y and X.L.; methodology, N.Y; software, N.Y and P.S.; validation, H.J., X.S. and A.C.; formal analysis, N.Y; investigation, N.Y; resources, X.L.; writing—original draft preparation, N.Y; writing—review and editing, X.L.; visualization, P.S.; supervision, X.L.; project administration, H.J., X.S. and A.C.

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Correspondence to Chen Aidong.

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This article is part of the Topical Collection: Special Issue on Affordable and Clean Energy

Guest Editors: Dajiang Chen, Ning Zhang, and Chunpeng Ge

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Ning, Y., Xiang, L., Hongyuan, J. et al. Insulator defect detection in complex scenarios based on cascaded networks with lightweight attention mechanism. Peer-to-Peer Netw. Appl. 17, 2123–2136 (2024). https://doi.org/10.1007/s12083-024-01682-2

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  • DOI: https://doi.org/10.1007/s12083-024-01682-2

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