Real‐time transmission tower detection from video based on a feature descriptor

Cerón, Alexander; Mondragón, Iván F.; Prieto, Flavio

doi:10.1049/iet-cvi.2015.0477

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…The system architecture is shown in Figure 4. Cerón et al [58] advanced a pylon detection method based on a line detection method and a grid of two-dimensional feature descriptors. Pylons could be characterized well by the descriptors.…”

Section: Traditional Object Detection Algorithmsmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

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Section: Traditional Object Detection Algorithmsmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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“…The tower detection process is done mainly using computer vision techniques based in machine learning methods such as neural networks, SVM [19,20] and recently deep learning [1]. The process requires a training stage.…”

Section: Tower Detection Processmentioning

confidence: 99%

Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose

Cerón¹,

Prieto²,

Mejias³

2019

Path Planning for Autonomous Vehicles - Ensuring Reliable Driverless Navigation and Control Maneuver

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In this chapter we present the development of automated visual inspection systems for electrical infrastructure. The inspection is performed using images acquired with an unmanned aerial vehicle (UAV). Through automated inspection routes, the state of the infrastructure can be evaluated and then the appropriate correcting measures be taken. The monitoring of power lines can be done using passive sensors such as cameras or active sensors such as light detection and ranging (LIDAR) cameras, image processing techniques, computer vision and control systems can then be used. Additionally, a three-dimensional (3D) reconstruction process is possible using images either offline or during the monitoring. An UAV with an onboard embedded computer is used to execute the computer vision and path planning algorithms. The work done shows that the proposed strategy aids in the automation of power line inspection.

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“…The status detection of electric power equipment is an essential technique for the high-voltage transmission lines inspection in which a wide variety of sensors are used [1]. Over the past few decades, vision sensor-based methods have been developed rapidly, and many topics have been examined, such as insulator detection [2], power line detection [3,4], and power tower detection [5,6]. Early studies show that the insulator string is one of the most important pieces of equipment of the high-voltage transmission line as it can provide both mechanical support and electrical insulation.…”

Section: Introductionmentioning

confidence: 99%

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

et al. 2019

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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Real‐time transmission tower detection from video based on a feature descriptor

Cited by 16 publications

References 22 publications

Environment Perception Technologies for Power Transmission Line Inspection Robots

Environment Perception Technologies for Power Transmission Line Inspection Robots

Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

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