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A System for Ammeter Inspection Task

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Published:23 January 2021Publication History

ABSTRACT

There are millions of ammeters working in China and daily manual inspection of their status consumes immense manpower and material resources. Besides, manual inspection is inevitably affected by factors such as the levels of evaluation skills and tiredness. To solve the problem, we propose an ammeter safety status assessment method and construct a software system for the ammeter inspection task, which encapsulates smart detection and evaluation algorithms for ammeter and its parts into an app and website. The use of this system has greatly reduced the workload of ammeter inspection tasks and unify the standards for defect judgment. Meanwhile, raw data and its analysis results are kept in the system and can be reused to improve the system further. The system has been deployed in the electric power system of Hangzhou and creates millions of economic values.

References

  1. Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]. 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Felzenszwalb P F, Girshick R B, McAllester D, Object detection with discriminatively trained part-based models[J]. IEEE transactions on pattern analysis and machine intelligence, 2009, 32(9): 1627-1645.Google ScholarGoogle Scholar
  3. Girshick R, Donahue J, Darrell T, Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.Google ScholarGoogle Scholar
  4. Uijlings J R R, Van De Sande K E A, Gevers T, Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2): 154-171.Google ScholarGoogle Scholar
  5. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.Google ScholarGoogle Scholar
  6. Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural processing letters, 1999, 9(3): 293-300.Google ScholarGoogle Scholar
  7. Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.Google ScholarGoogle Scholar
  8. Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99.Google ScholarGoogle Scholar
  9. Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.Google ScholarGoogle Scholar
  10. Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.Google ScholarGoogle Scholar
  11. Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.Google ScholarGoogle Scholar
  12. Howard A G, Zhu M, Chen B, Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ICCCV '20: Proceedings of the 3rd International Conference on Control and Computer Vision
    August 2020
    114 pages
    ISBN:9781450388023
    DOI:10.1145/3425577

    Copyright © 2020 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 23 January 2021

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