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Cited by 39Power Line Inspection RobotsInfrastructure Maintenance and MonitoringAdvanced Neural Network Applications

UAV Visual and Thermographic Power Line Detection Using Deep Learning

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Santos, Tiago, Cunha, Tiago, Dias, André, Moreira, António Paulo, Almeida, José·2024·Sensors
DOI 10.3390/s24175678PMC PMC11397857
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Abstract

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

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  • The first studies, especially [6,9], provide an important basis for better understanding the thermographic inspection technique using drones, also showing how image processing helps identify defects.

    “The first studies, especially [6,9], provide an important basis for better understanding the thermographic inspection technique using drones, also showing how image processing helps identify defects, which are delegated to maintenance teams to resolve.”

    From Multicriteria Methodology for Prioritizing Predictive Maintenance Using RPASs (Drones) with Thermal Cameras on Transmission Lines (2025) · cites this paper as [9]

    The Introduction describes UAV thermographic inspection and says thermal images are commonly used to monitor faults such as hot spots. However, the Implemented Image Processing Approach (section 3) trains its models to detect power lines, not defects; it does not show that its image processing identifies defects for maintenance teams. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Publications [4,5,6,7,8,9,10] address techniques for carrying out preventive inspections on transmission lines using RPASs with thermal cameras, demonstrating the importance of using this technology and the impact of using aircraft to carry out inspections.

    “Publications [4,5,6,7,8,9,10] address techniques for carrying out preventive inspections on transmission lines using RPASs with thermal cameras, demonstrating the importance of using this technology and the impact of using aircraft to carry out inspections.”

    From Multicriteria Methodology for Prioritizing Predictive Maintenance Using RPASs (Drones) with Thermal Cameras on Transmission Lines (2025) · cites this paper as [9]

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Authors
Santos, T., Cunha, T., Dias, A., Moreira, A. P., & Almeida, J.
Affiliations
  • INESC TEC (PT)
  • Universidade Técnica do Porto (PT)
  • Universidade do Porto (PT)
  • INESCTEC—Institute for Systems and Computer Engineering, Technology and Science, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Corresponding author
Santos, Tiago
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2024-08-31
Volume
24
Issue
17
Pages
5678
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s24175678
PMC
PMC11397857
OpenAlex
W4402126439
Cited by
39
References
2
Citations by year
2026: 20 · 2025: 19
FWCI
5.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia (UIDB/50014/2020)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Power Line Inspection Robots
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
power line detection, UAV inspection, infrared thermography, YOLOv8, power line inspection, obstacle detection, on-board processing

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