Analysis of the recognition and localisation techniques of power transmission lines components in aerial images acquired by drones

Homma, Rafael Zimmermann; Sohn, Odyr; Bose, Ricardo Chessini

doi:10.1049/oap-cired.2017.1193

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…The researchers are working on different kinds of algorithms for line detection and the extraction of different features from aerial images (Zhao et al, 2017). Some are working on the separation and fault detection algorithm for aerial images (Homma et al, 2017;Qinggang Wu et al, 2012). The vision-based technology is also implemented in the detection of different defects like railheads and metallic conductors (Yuan Li et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Zaidi¹,

Tariq²

2023

S. F. J. of Dev.

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Due to the growing need for electricity, the effective inspection of the power lines is becoming an important matter. In this paper, the author presents the inspection of power or transmission line with autonomous automatic UAVs (Unmanned Aerial Vehicles). For the comprehensive inspection of power lines and its different components, such as (cross arms, cracks in poles, rot damage, and insulator burn), It is needed to inspect from every side of the elements and the masts. So, the angle and speed of the drone are much more important to take images while moving around the poles. The simulator used for the experiments, including deep learning models, acts as a vital source of data analysis. At the same time, pictures are used as the primary data source. Through the Deep learning method, a suggestion of action generated for the movement around the masts. The use of a simulator is a quick, accurate, and inexpensive solution, with less real/world factors affecting the inspection process, such as weather, time, and cost of using many different resources. This study presents experiments with lightweight deep learning models through developing the prototype of vision based unmanned aerial vehicle to inspect the power line in a simulated environment. It focuses on the large demand of power companies to inspect the power line autonomously with the influence of deep learning. Finally, several deep learning models are compared when inspection along the power lines. The model shows satisfactory results in the testing path. The model trained by MobileNetV2 performs best among all other models.

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Section: Introductionmentioning

confidence: 99%

Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Zaidi¹,

Tariq²

2023

S. F. J. of Dev.

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“…The UAV-assisted power grid patrol can effectively solve the problems of traditional manual patrol, such as large workload, low efficiency, high risk, high cost of manned helicopter patrol, complex flight approval procedures, and limited operating environment. With the development of application technology in the UAV industry [10,11,12], UAV-assisted power grid patrol will provide a safe, efficient and comprehensive patrol mode for power grid patrol to meet the needs of daily power grid patrol, fault patrol, and emergency rescue.…”

Section: Introductionmentioning

confidence: 99%

Design of Power Grid Intelligent Patrol Operation and Maintenance System Based on Multi-Rotor UAV Systems

Liu¹,

Hai-feng²,

Ni³

et al. 2020

Studies in Applied Electromagnetics and Mechanics

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Complex geographical conditions and bad weather have caused great difficults to inspect transmission line of power grids. This work aims to develop a micro multi-rotor Unmanned Aerial Vehicle (UAV) system for inspecting power grid. The proposed system integrates with an intelligent robot and mobile communication networks. The high-resolution aerial images of the inspection line can be obtained to improve the operational efficiency and safety. The time period from the front-end signal acquisition to terminal decoding and playback is 1.5s for condition-based maintenance. The system supports TDD-LTE, FDD-LTE, WCDMA, CDMA2000, and other network standards. It also supports the video input signals with the resolution of standard 576i, HD 720p, FHD 1080i, and FHD 1080p. It is compatible with a variety of transmission formats and codes, help to achieve timely, comprehensive and efficient high-resolution aerial image acquisition of patrol lines, and improve operational efficiency and safety, real-time monitoring and condition-based maintenance of power transmission lines.

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“…Some researchers have put forward different kinds of algorithms for power lines detection and extraction in aerial images [13]- [16]. Some others are focus on the segmentation and fault detection algorithm for aerial insulator images [17]- [21] and also the detection of bird's nest in high power lines [22]. The vision-based technology has also been used in defect detection of the weld bead and of rail heads, and in the measurement of the lay length of metallic conductor [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

A Recognition Technology of Transmission Lines Conductor Break and Surface Damage Based on Aerial Image

et al. 2019

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The aluminum strands break and surface damage will gradually develop into irreversible destruction to the conductors and the whole transmission lines, and even lead to serious power outages. In order to find out the potential strands break and damage faults and prevent its further deterioration, a recognition method of conductor break and surface defects in transmission lines' unmanned aerial vehicle (UAV) inspection is presented in this paper. First, a conductor image is obtained by the UAV image acquisition system, and then, the conductor region is extracted by the adaptive threshold segmentation after the enhancement processing by the gray variance normalization method (GVN). Second, the conductor break is detected by the square wave transformation (SWT) of its grayscale distribution curves, which is simple and effective. Meanwhile, the conductor surface defects are identified by the projection algorithm of the GVN image of the conductor region. Finally, calculating the number of broken strands and filtering the suspect defects, the final fault diagnosis results can be obtained. We analyze the performance of the technology by a series of experiments, and the results demonstrate that the proposed method can measure the conductor break and surface defects faults with the average accuracy of 90.45% and 92.05%, respectively. The health condition of the conductor can be monitored based on the method presented in this paper, by which the safe operation of transmission lines can be guaranteed. INDEX TERMSConductor break, surface defects, image processing, transmission lines, UAV inspection.

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Analysis of the recognition and localisation techniques of power transmission lines components in aerial images acquired by drones

Cited by 7 publications

References 4 publications

Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Design of Power Grid Intelligent Patrol Operation and Maintenance System Based on Multi-Rotor UAV Systems

A Recognition Technology of Transmission Lines Conductor Break and Surface Damage Based on Aerial Image

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