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Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

Author

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  • Zhenbing Zhao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Zhen Zhen

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Lei Zhang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yincheng Qi

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yinghui Kong

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Ke Zhang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.

Suggested Citation

  • Zhenbing Zhao & Zhen Zhen & Lei Zhang & Yincheng Qi & Yinghui Kong & Ke Zhang, 2019. "Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN," Energies, MDPI, vol. 12(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1204-:d:217852
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    Citations

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    Cited by:

    1. Chuanyang Liu & Yiquan Wu & Jingjing Liu & Jiaming Han, 2021. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images," Energies, MDPI, vol. 14(5), pages 1-19, March.
    2. 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.
    3. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    4. Jingjing Liu & Chuanyang Liu & Yiquan Wu & Huajie Xu & Zuo Sun, 2021. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images," Energies, MDPI, vol. 14(14), pages 1-19, July.
    5. Linfeng Wang & Heng Wan & Deqing Huang & Jiayao Liu & Xuliang Tang & Linfeng Gan, 2023. "Sustainable Analysis of Insulator Fault Detection Based on Fine-Grained Visual Optimization," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    6. Zahid Ali Siddiqui & Unsang Park, 2020. "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique," Energies, MDPI, vol. 13(13), pages 1-24, June.
    7. Ju Sik Kim & Kyu Nam Choi & Sung Woo Kang, 2021. "Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    8. Jiaming Han & Zhong Yang & Hao Xu & Guoxiong Hu & Chi Zhang & Hongchen Li & Shangxiang Lai & Huarong Zeng, 2020. "Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images," Energies, MDPI, vol. 13(3), pages 1-20, February.
    9. Zhaoyun Zhang & Shihong Huang & Yanxin Li & Hui Li & Houtang Hao, 2022. "Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning," Energies, MDPI, vol. 15(7), pages 1-17, March.
    10. Juping Gu & Junjie Hu & Ling Jiang & Zixu Wang & Xinsong Zhang & Yiming Xu & Jianhong Zhu & Lurui Fang, 2023. "Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s," Energies, MDPI, vol. 16(6), pages 1-18, March.

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