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Assets management on electrical grid using Faster-RCNN

Author

Listed:
  • Jules Raymond Kala

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Didier Michael Kre

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Armelle N’Guessan Gnassou

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Jean Robert Kamdjoug Kala

    (Universite Catholique d’Afrique Centrale)

  • Yves Melaine Akpablin Akpablin

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Tiorna Coulibaly

    (CIE-Centre de Recherche en Intelligence Artificielle)

Abstract

Electrical utility companies around the world are keeping track of all equipment on their distribution grid, because it will help them improve the management and the quality of the services they offer to their customers. Asset management of the electric grid is usually conducted manually, which is expensive, time consuming and the results obtained are often not accurate. In this article an automated asset management system for electricity, transport infrastructures is proposed, it is based on images taken by drones and analysed by Faster Region proposal Convolutional Neural Networks (Faster-RCNN) to generate the inventory. The designs of CNN are inspired from the human brain structures, they have been applied to many fields such as object recognition and crowed counting with promising results that are proven to be better than human observer. In order to evaluate the proposed asset management approach, a sample of images was randomly selected from a given dataset, the inventory results generated by the CNN based model are accurate, faster and cheaper than the previous approach based on human observers and helicopters.

Suggested Citation

  • Jules Raymond Kala & Didier Michael Kre & Armelle N’Guessan Gnassou & Jean Robert Kamdjoug Kala & Yves Melaine Akpablin Akpablin & Tiorna Coulibaly, 2022. "Assets management on electrical grid using Faster-RCNN," Annals of Operations Research, Springer, vol. 308(1), pages 307-320, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03650-4
    DOI: 10.1007/s10479-020-03650-4
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    References listed on IDEAS

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