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A Novel Transmission Line Insulator Detection Method Based on Enhanced YOLO Model

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Published:31 December 2021Publication History

ABSTRACT

Aerial photography patrol has become the main method of electric power patrol on transmission lines instead of manual patrol, and the integrity of insulators on transmission lines directly affects the power supply reliability. Under the interference of complex background, traditional insulator detection method tends to have a low ability to perform the detection tasks. To solve this problem, we proposed a new method based on enhanced YOLOv4. Based on watershed algorithm and data augmentation combinations, the problem of accurate image detection of aerial photo insulator is studied. Combined with the watershed algorithm and data augmentation combinations, the enhanced YOLO model has robust ability against different distances, various lighting conditions in complex environments. It is shown that our proposed method achieved competitive performance in the experiments.

References

  1. Wang Y, Wang J, Gao F, et al. Detection and Recognition for Fault Insulator Based on Deep Learning[C]// 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).Google ScholarGoogle Scholar
  2. Miao X, Liu X, Chen J, et al. Insulator Detection in Aerial Images for Transmission Line Inspection Using Single Shot Multibox Detector [J]. IEEE Access, 2019:1--1.Google ScholarGoogle Scholar
  3. Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137--1149.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517--6525.Google ScholarGoogle Scholar
  5. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015.Google ScholarGoogle Scholar
  6. Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[J]. European Conference on Computer Vision, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  7. Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J]. arXiv e-prints, 2018.Google ScholarGoogle Scholar
  8. Bochkovskiy A, Wang C Y, Liao H. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. 2020.Google ScholarGoogle Scholar
  9. Liu S, Qi L, Qin H, et al. Path Aggregation Network for Instance Segmentation[J]. IEEE, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  10. He K, Zhang X, Ren S, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 37(9):1904--16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Lin T Y, Dollar P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2017.Google ScholarGoogle Scholar
  12. Bieniek A, Moga A. An efficient watershed algorithm based on connected components [J]. Pattern Recognition, 2000, 33(6):907--916.Google ScholarGoogle ScholarCross RefCross Ref
  13. Liu Y, Huang X. Research on Insulator Burst Fault Identification Based on YOLOv4 and Improved Watershed Algorithm [J]. Power System and Clean Energy, 2021, 5 (7): 51--57.Google ScholarGoogle Scholar
  14. Wang S, "An Augmentation Small Object Detection Method Based on NASFPN," 2020 7th International Conference on Information Science and Control Engineering (ICISCE), 2020, pp. 213--218, doi: 10.1109/ICISCE50968.2020.00054.Google ScholarGoogle ScholarCross RefCross Ref

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  1. A Novel Transmission Line Insulator Detection Method Based on Enhanced YOLO Model

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    • Published in

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      EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
      October 2021
      1723 pages
      ISBN:9781450384322
      DOI:10.1145/3501409

      Copyright © 2021 ACM

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      Publication History

      • Published: 31 December 2021

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      EITCE '21 Paper Acceptance Rate294of531submissions,55%Overall Acceptance Rate508of972submissions,52%
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