Detection system of damaged cables using video obtained from an aerial inspection of transmission lines

Ishino, Ryuichi; Tsutsumi, Fujio

doi:10.1109/pes.2004.1373201

Cited by 33 publications

(11 citation statements)

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“…A method for detecting cables damaged by lightning strokes was proposed in [47]. To begin with, the statistical analysis of the brightness of the cable, which is based on the mean brightness of the cable and its standard deviation, was performed to detect arc marks.…”

Section: Mapping and Inspection Of Power Line Componentsmentioning

confidence: 99%

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Nguyen

Jenssen

Roverso³

2018

International Journal of Electrical Power & Energy Systems

390

117

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To maintain the reliability, availability, and sustainability of electricity supply, electricity companies regularly perform visual inspections on their transmission and distribution networks. These inspections have been typically carried out using foot patrol and/or helicopter-assisted methods to plan for necessary repair or replacement works before any major damage, which may cause power outage. This solution is quite slow, expensive, and potentially dangerous. In recent years, numerous researches have been conducted to automate the visual inspections by using automated helicopters, flying robots, and/or climbing robots. However, due to the high accuracy requirements of the task and its unique challenges, automatic vision-based inspection has not been widely adopted. In this paper, with the aim of providing a good starting point for researchers who are interested in developing a fully automatic autonomous vision-based power line inspection system, we conduct an extensive literature review. First, we examine existing power line inspection methods with special attention paid to highlight their advantages and disadvantages. Next, we summarize well-suited tasks and review potential data sources for automatic vision-based inspection. Then, we survey existing automatic vision-based power line inspection systems. Based on that, we propose a new automatic autonomous vision-based power line inspection concept that uses Unmanned Aerial Vehicle (UAV) inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis and inspection. Then, we present an overview of possibilities and challenges of deep vision (deep learning for computer vision) approaches for both UAV navigation and UAV inspection and discuss possible solutions to the challenges. Finally, we conclude the paper with an outlook for the future of this field and propose potential next steps for implementing the concept.

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Section: Mapping and Inspection Of Power Line Componentsmentioning

confidence: 99%

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Nguyen

Jenssen

Roverso³

2018

International Journal of Electrical Power & Energy Systems

390

117

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“…An artificial vision system mounted in an UAV has the potential to fill this gap, by providing a cheap and flexible way to gather the transmission line information while carrying out the inspection process [40,41]. The images are processed with the purpose of detecting faults [42] and vegetation [43] or to extract power-lines for analysis [44,45]. Power wires can be detected using a line segment detection algorithm [46] or Hough transform with parallel constraint [47].…”

Section: Industrial Prototypesmentioning

confidence: 99%

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

2019

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Unmanned aerial vehicles (UAVs) are an emerging and promising alternative for monitoring of transmission lines in terms of flexibility, complexity, working speed, and cost. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the UAV placement with respect to the transmission grid. This work faces this challenge by developing a transmission line autonomous tracking system, which allows the placement of a commercial drone over a transmission grid using a monocular camera. This feature provides accurate positioning for the vehicle even where the Global navigation satellite system (GNSS) signal is denied, enabling to report the status of transmission lines, at any time. The system isolates transmission grid conductors in each acquired RGB-image using an image-processing algorithm based on Hough transform, morphological operations, and Gabor filters. With this information, the system computes the location of the UAV using a geometric approach that relates transmission lines building parameter and optical geometry. However, it has the problem of gradual error accumulation when the drone moves. In this regards, the estimated position of the drone is computed by the maximum likelihood estimation (MLE) by the position information estimated by visual-system, the inertial measurement unit (IMU) and GNSS. The proposed positioning system showed an efficiency of 91.44% in field experimentation in the extraction of transmission conductor, with a root mean square the error of 0.18 m in the UAV localization.

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“…However, these methods are not suitable for live wires, and can only indicate failures in the line, without specifying location. More commonly employed is visual inspection performed from helicopters, both manned [6] and tele-operated [7].…”

Section: A Sensing Technologiesmentioning

confidence: 99%

Sensor-Arm — robotic manipulator for preventive maintenance and inspection of high-voltage transmission lines

Debenest

Guarnieri

Takita

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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There have been several works proposing mobile robots for inspection of high-voltage transmission lines. Those studies addressed mostly locomotion issues, with little attention paid to the sensing task. However, the deployment of sensors around the cables is not an elementary operation. This work indicates which sensors may be most suitable for the inspection of transmission lines, and describes their assembly in a simple and compact structure (Sensor-Unit). In addition, it proposes several manipulator mechanisms (Sensor-Arm) to deploy sensors around the cables and to remove them when necessary. The mechanisms are compared taking into account practical aspects such as assembly, reliability, functionality and results of field experiments. Finally, the design of a high-quality camera for detailed inspection is also described.

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Detection system of damaged cables using video obtained from an aerial inspection of transmission lines

Cited by 33 publications

References 1 publication

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Sensor-Arm — robotic manipulator for preventive maintenance and inspection of high-voltage transmission lines

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Detection system of damaged cables using video obtained from an aerial inspection of transmission lines

Cited by 33 publications

References 1 publication

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Sensor-Arm &#x2014; robotic manipulator for preventive maintenance and inspection of high-voltage transmission lines

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Product

Resources

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Sensor-Arm — robotic manipulator for preventive maintenance and inspection of high-voltage transmission lines