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A light defect detection algorithm of power insulators from aerial images for power inspection

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Abstract

With the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a common defect of power insulators, missing-cap issue will affect the structural strength and durability of different power insulators. Therefore, the condition monitoring of power insulators is a daily but priority power line inspection task. Faced with the weak image features of small insulator defects in the aerial images, the conventional handcrafted features could not extract effectively powerful image features. Meanwhile, the small-scale insulator defects will bring a certain effect to the model training of deep learning. Therefore, the high-efficiency and accurate defect inspection still present a challenging task against complex backgrounds. To address the above issues, aimed at the missing-cap defects of power insulators, a novel defect identification algorithm from aerial images is proposed by taking advantage of state-of-the-art deep learning and transfer learning models. Fused with Spatial Pyramid Pooling (SPP) and MobileNet networks, a light deep convolutional neural network (DCNN) model based on You Only Look Once (YOLO) V3 network is proposed for fast and accurate insulator location to remove complex background interference. On the basis, combined with Dempster–Shafer (DS) evidence theory, the improved transfer learning model based on feature fusion is proposed for high-precision defect identification of power insulators. Experiments show that the proposed method could acquire a better identification performance against complex power inspection environment compared with other related detection models.

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Acknowledgements

The authors wish to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key Research & Development Project of China (2020YFB1313701), the National Natural Science Foundation of China (No.62003309), Science & Technology Research Project in Henan Province of China (No.202102210098), and Outstanding Foreign Scientist Support Project in Henan Province of China (No. GZS2019008).

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  1. Lei Yang and Junfeng Fan have contributed equally to this work.

Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, 450001, Zhengzhou, China

    Lei Yang, Shouan Song & Yanhong Liu

  2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Junfeng Fan

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  1. Lei Yang
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  2. Junfeng Fan
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Correspondence to Yanhong Liu.

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Yang, L., Fan, J., Song, S. et al. A light defect detection algorithm of power insulators from aerial images for power inspection. Neural Comput & Applic 34, 17951–17961 (2022). https://doi.org/10.1007/s00521-022-07437-5

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