IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v385y2025ics0306261925002375.html
   My bibliography  Save this article

Deep learning in automated power line inspection: A review

Author

Listed:
  • Faisal, Md. Ahasan Atick
  • Mecheter, Imene
  • Qiblawey, Yazan
  • Fernandez, Javier Hernandez
  • Chowdhury, Muhammad E.H.
  • Kiranyaz, Serkan

Abstract

In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of an extensive collection of videos and images has become essential for maintaining the reliability, safety, and sustainability of electricity transmission. A significant focus on applying deep learning techniques for enhancing power line inspection processes has been observed in recent research. A comprehensive review of existing studies has been conducted in this paper, to aid researchers and industries in developing improved deep learning-based systems for analyzing power line data. The conventional steps of data analysis in power line inspections have been examined, and the body of current research has been systematically categorized into two main areas: the detection of components and the diagnosis of faults. A detailed summary of the diverse methods and techniques employed in these areas has been encapsulated, providing insights into their functionality and use cases. Special attention has been given to the exploration of deep learning-based methodologies for the analysis of power line inspection data, with an exposition of their fundamental principles and practical applications. Moreover, a vision for future research directions has been outlined, highlighting the need for advancements such as edge–cloud collaboration, and multi-modal analysis among others. Thus, this paper serves as a comprehensive resource for researchers delving into deep learning for power line analysis, illuminating the extent of current knowledge and the potential areas for future investigation.

Suggested Citation

  • Faisal, Md. Ahasan Atick & Mecheter, Imene & Qiblawey, Yazan & Fernandez, Javier Hernandez & Chowdhury, Muhammad E.H. & Kiranyaz, Serkan, 2025. "Deep learning in automated power line inspection: A review," Applied Energy, Elsevier, vol. 385(C).
    • Handle: RePEc:eee:appene:v:385:y:2025:i:c:s0306261925002375
    DOI: 10.1016/j.apenergy.2025.125507
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925002375
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125507?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yao Liu & Jianmai Shi & Zhong Liu & Jincai Huang & Tianren Zhou, 2019. "Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone," Energies, MDPI, vol. 12(7), pages 1-20, April.
    2. Chunhe Song & Wenxiang Xu & Zhongfeng Wang & Shimao Yu & Peng Zeng & Zhaojie Ju, 2020. "Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection," Complexity, Hindawi, vol. 2020, pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. James Campbell & Ángel Corberán & Isaac Plana & José M. Sanchis & Paula Segura, 2022. "Polyhedral analysis and a new algorithm for the length constrained K–drones rural postman problem," Computational Optimization and Applications, Springer, vol. 83(1), pages 67-109, September.
    3. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    4. Ahmed Daeli & Salman Mohagheghi, 2022. "Power Grid Infrastructural Resilience against Extreme Events," Energies, MDPI, vol. 16(1), pages 1-17, December.
    5. Faten Aljalaud & Heba Kurdi & Kamal Youcef-Toumi, 2023. "Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior," Mathematics, MDPI, vol. 11(9), pages 1-23, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:385:y:2025:i:c:s0306261925002375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.