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Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN

Author

Listed:
  • Ziquan Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Huifang Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

To detect the categories and positions of various transformer components in inspection images automatically, this paper proposes a transformer component detection model with high detection accuracy, based on the structure of Faster R-CNN. In consideration of the significant difference in component sizes, double feature maps are used to adapt to the size change, by adjusting two weights dynamically according to the object size. Moreover, different from the detection of ordinary objects, there is abundant useful information contained in the relative positions between components. Thus, the relative position features are defined and introduced to the refinement of the detection results. Then, the training process and detection process are proposed specifically for the improved model. Finally, an experiment is given to compare the accuracy and efficiency of the improved model and the original Faster R-CNN, along with other object detection models. Results show that the improved model has an obvious advantage in accuracy, and the efficiency is significantly higher than that of manual detection, which suggests that the model is suitable for practical engineering applications.

Suggested Citation

  • Ziquan Liu & Huifang Wang, 2018. "Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN," Energies, MDPI, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3496-:d:190664
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    References listed on IDEAS

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    1. Jingjing Wang & Junhua Wang & Jianwei Shao & Jiangui Li, 2017. "Image Recognition of Icing Thickness on Power Transmission Lines Based on a Least Squares Hough Transform," Energies, MDPI, vol. 10(4), pages 1-15, March.
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    Cited by:

    1. Hongchen Li & Zhong Yang & Jiaming Han & Shangxiang Lai & Qiuyan Zhang & Chi Zhang & Qianhui Fang & Guoxiong Hu, 2020. "TL-Net: A Novel Network for Transmission Line Scenes Classification," Energies, MDPI, vol. 13(15), pages 1-15, July.

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