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Electrical insulator defect detection with incomplete annotations and imbalanced samples
  • Fengqian Pang,
  • Chunyue Lei,
  • jingsheng zeng
Fengqian Pang
North China University of Technology

Corresponding Author:[email protected]

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Chunyue Lei
North China University of Technology
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jingsheng zeng
North China University of Technology
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Insulators are one of the key components in high-voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, Unmanned Aerial Vehicle (UAV) inspection is more widely used, facilitating incorporation of CNN-based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. In this paper, we focus on insulator defect detection with imbalanced data and incomplete annotations. Our proposed framework, named Pi-Index, introduces Positive Unlabeled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.
21 Aug 2023Submitted to IET Generation, Transmission & Distribution
22 Aug 2023Assigned to Editor
22 Aug 2023Submission Checks Completed
24 Aug 2023Review(s) Completed, Editorial Evaluation Pending
04 Sep 2023Reviewer(s) Assigned
27 Oct 2023Editorial Decision: Revise Major