A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images

Zhao, Wenbo; Qian, Dong; Zuo, Zhengli

doi:10.3390/rs14061367

Cited by 18 publications

(6 citation statements)

References 34 publications

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“…The main function of the communication system is to transmit data, which is the bridge between the drone and the remote control. [29][30][31] A remote control receiver is set inside the UAV to complete the UAV's reception and feedback of information. In particular, the fouraxis UAV must have at least four channels to transmit signals so as to control the front, rear, left and right four groups of suspension shafts and motors respectively.…”

Section: Airspace Patrolmentioning

confidence: 99%

Research on vision-based UAV power line detection technology

Wang

2023

Preprint

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In order to ensure the reliability and safety of the power grid operation, the power company will carry out regular inspection of the transmission line network. The traditional inspection method is generally manual inspection by foot patrol, which is not only slow but also time-consuming and laborious. In recent years, with the development of unmanned aerial vehicle(UAV) and unmanned vehicle control technology and the improvement of inspection accuracy, UAV technology has been widely used in the intelligent inspection of power grid. In order to ensure the reliability and safety of power grid operation in the complex and huge power grid system, in order to find the fault of transmission line as soon as possible and give feedback, The UAV detection technology with image recognition technology has become an urgent need for the development of power system. The vision-based UAV power line detection technology has a wide range of application prospects. In this paper, the relevant literature in recent years is reviewed extensively, and the status quo of vi-sion-based UAV detection technology is reviewed. This paper summarizes the existing power line detection methods, the system structure of UAV power line detection and the image recognition technology of data acquisition, and focuses on the development of transmission line data processing technology based on deep learning, in order to provide a starting point for researchers to develop automatic autonomous intelligent inspection of transmission line system. Finally, the development of this field and the next possible challenges are discussed, and the future is prospected.

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Section: Airspace Patrolmentioning

confidence: 99%

Research on vision-based UAV power line detection technology

Wang

2023

Preprint

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“…Zhang et al (2021) proposed an ultra-lightweight and ultra-fast abnormal target identification network for transmission line, but it could not extract the power line. Zhao et al (2022) proposed a method combining line detection and semantic segmentation for power line extraction from unmanned aerial vehicle images; however, this method was more complicated and did not have real-time performance. Zhang et al (2019) proposed a method with convolutional features and structured constraints, and two algorithms were used to extract feature fusion for feature fusion, although such a method is also time consuming and cannot be used for real-time UAV power line extraction.…”

Section: Introductionmentioning

confidence: 99%

Gabor-YOLONet: A lightweight and efficient detection network for low-voltage power lines from unmanned aerial vehicle images

Feng

Zhang²,

Zepu³

et al. 2023

Front. Energy Res.

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Maintaining and monitoring low-voltage overhead power lines are of great importance because such lines are the key link between the power system and low-voltage power users. At present, few networks can be detected accurately on intelligent edge identification devices because of the complex backgrounds and limited characteristics in unmanned aerial vehicle images as well as the low computing abilities of hardware. In order to give consideration to accuracy and speed, a novel power line detection method was proposed, denoted by Gabor-YOLONet, used for intelligent edge identification devices available to UAV. Unlike existing methods, the proposed method uses the Gabor algorithm to extract foreground of power lines from cluttered backgrounds automatically and predict power lines and their auxiliary targets such as insulators in the foreground scene. In addition, a new inference method was introduced, which can summarize the average location and orientation of auxiliary targets by clustering to verify the rationality of the predicted results for power lines. The experiment results showed that the proposed method had the higher accuracy and consumed less computing resources; compared with other methods, the mAP of identification for power lines was 86.6% and the running time was only 25 ms, with excellent performance on intelligent edge devices.

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“…Unfortunately, like any system, the power grid is subject to the ageing process, which may result in unforeseen interruptions in its functioning. The first step in the process of visual diagnostics of high-voltage lines is always the detection of its key elements [ 6 , 7 , 8 , 10 , 11 ]. The traditional manual inspection methods are usually not fast enough and often do not provide sufficiently detailed detection data [ 2 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The occurrence of a blackout can cause significant financial losses and, in extreme cases, a humanitarian disaster. Many authors [ 2 , 10 , 13 , 14 , 15 , 16 , 17 ] see the solution to this problem in the use of UAVs and broadly understood methods of visual inspection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Power Line Insulators in Digital Images Based on the Transformed Colour Intensity Profiles

Tomaszewski

Gasz

Osuchowski

2023

Sensors

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Proper maintenance of the electricity infrastructure requires periodic condition inspections of power line insulators, which can be subjected to various damages such as burns or fractures. The article includes an introduction to the problem of insulator detection and a description of various currently used methods. Afterwards, the authors proposed a new method for the detection of the power line insulators in digital images by applying selected signal analysis and machine learning algorithms. The insulators detected in the images can be further assessed in depth. The data set used in the study consists of images acquired by an Unmanned Aerial Vehicle (UAV) during its overflight along a high-voltage line located on the outskirts of the city of Opole, Opolskie Voivodeship, Poland. In the digital images, the insulators were placed against different backgrounds, for example, sky, clouds, tree branches, elements of power infrastructure (wires, trusses), farmland, bushes, etc. The proposed method is based on colour intensity profile classification on digital images. Firstly, the set of points located on digital images of power line insulators is determined. Subsequently, those points are connected using lines that depict colour intensity profiles. These profiles were transformed using the Periodogram method or Welch method and then classified with Decision Tree, Random Forest or XGBoost algorithms. In the article, the authors described the computational experiments, the obtained results and possible directions for further research. In the best case, the proposed solution achieved satisfactory efficiency (F1 score = 0.99). Promising classification results indicate the possibility of the practical application of the presented method.

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A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images

Cited by 18 publications

References 34 publications

Research on vision-based UAV power line detection technology

Research on vision-based UAV power line detection technology

Gabor-YOLONet: A lightweight and efficient detection network for low-voltage power lines from unmanned aerial vehicle images

Detection of Power Line Insulators in Digital Images Based on the Transformed Colour Intensity Profiles

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