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YOLOX with CBAM for insulator detection in transmission lines

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Abstract

The traditional manual inspection mode is inefficient for detecting transmission line insulators. Even in the case of the detection system generated by combining aerial images by unmanned aerial vehicles with traditional machine vision algorithms, the detection accuracy and response speed have been increasingly unable to meet the requirements of modern power-grid construction. However, with the development of deep learning image processing technology, its deep level neural network can simulate the human brain to automatically extract the rich feature expression of the insulator image coupled with network training to quickly provide the final recognition results, improving the detection performance of the insulator defect detection technology based on this optimization method. Therefore, this study uses the deep learning object detection network YOLOX to classify and locate transmission line insulators. Accordingly, this study introduces the convolutional block attention module (CBAM) theory to optimize the YOLOX network, further enhancing the performance of the network model. The experimental results show that after introducing the CBAM, the detection accuracy of YOLOX on the insulator dataset herein has been improved by ~ 3% and the performance of the model has been optimized to some extent.

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All data generated or analysed during this study are included in this published article.

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Acknowledgements

This work was sponsored by Shanxi Provincial Higher Education Science and Technology Innovation Project (Grant no. 2022L524) and Shanxi Provincial Basic Research Program(Grant no. 202103021223048).

Funding

There was no specific funding for this study.

Author information

Author notes

  1. Kai Yang, Yu Zhang, Xin Zhang and Lu Zheng contributed to this work.

Authors and Affiliations

  1. Department of Automation, Taiyuan Institute of Technology, Taiyuan, 030008, China

    Kai Yang, Yu Zhang & Xin Zhang

  2. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Yu Zhang

  3. Shanxi Energy Internet Research Institute, Taiyuan, 030006, China

    Yu Zhang

  4. School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Lu Zheng

Authors
  1. Kai Yang
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  2. Yu Zhang
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  3. Xin Zhang
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  4. Lu Zheng
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Correspondence to Kai Yang.

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Yang, K., Zhang, Y., Zhang, X. et al. YOLOX with CBAM for insulator detection in transmission lines. Multimed Tools Appl 83, 43419–43437 (2024). https://doi.org/10.1007/s11042-023-17245-1

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