Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

Zhao, Le; Wang, Xianpei; Yao, Hongtai; Tian, Meng; Jian, Zini

doi:10.1109/access.2019.2939025

Cited by 25 publications

(11 citation statements)

References 46 publications

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“…The most basic but the most important problem in the vision-based UAV power proposed to detect line segments by morphological filtering an edge map image in the local criterion and group the line segments into whole power line in the global criterion. In [14] proposed a novel object-based Markov random field with anisotropic weighted penalty method to distinguish the power line segments. And an envelope-based piecewise fitting method to fit the power line.…”

Section: Research Vision-based Uav Distribution Line Inspection Using...mentioning

confidence: 99%

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Chen

Zhuang

Wang

et al. 2022

Preprint

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Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.

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Section: Research Vision-based Uav Distribution Line Inspection Using...mentioning

confidence: 99%

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Chen

Zhuang

Wang

et al. 2022

Preprint

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“…It is difficult to extract aerial images of transmission lines because of the weak target which is a thin line and the complex background which ranges from rugged terrain to continuous vegetation [58]. An object based Markov Random field with anisotropic weighted penalty is proposed in [57] to distinguish transmission line segments from other line segments against varying background. A line fault is detected using basic image processing techniques with varying background in [58].…”

Section: Surveillance Of Transmission Linesmentioning

confidence: 99%

Leveraging the Technology of Unmanned Aerial Vehicles for Developing Countries

2020

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The technology of unmanned aerial vehicles (UAVs) renders itself easily to a variety of applications because of their characteristics such as flexibility, quick deployment and cost effectiveness. Developing countries can harness UAV technology for humanitarian and development goals. This paper presents five interesting UAV applications for developing countries and these include disaster management, agriculture, delivery, surveillance of transmission lines and network coverage. UAVs aid the process of disaster management because they can be quickly deployed to take imagery which is used by rescue teams. They offer a cheap alternative to satellites and manned aircraft for agricultural mapping and management. Developing countries are faced with the major challenge of inadequate infrastructure which can be overcome by using UAVs to make especially emergency deliveries. UAVs are more effective at inspecting power transmission lines than manual techniques especially in developing countries where it is expected that the power transmission lines will span over tens of thousands of kilometres. Network coverage can be achieved cost effectively using UAVs for rural remote areas that are neglected by network operators because of their low economic return. This paper also discusses challenges that must be overcome to fully exploit UAV technology for the mentioned applications. Additionally, this paper presents interventions by African governments towards the development of UAV technology.

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“…However, when the image is inconsistent with the pre-set context information, the accuracy of these algorithms may decline rapidly. Zhao et al [20] first used LSD to extract line segments, regarded each line segment as a node to establish an irregular graph model, and then proposed an object-based Markov random field (OMRF) with an anisotropic weighted penalty to realize the classification of power lines. This method considers power line extraction as an image segmentation task and can achieve good results.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.

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Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

Cited by 25 publications

References 46 publications

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Leveraging the Technology of Unmanned Aerial Vehicles for Developing Countries

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

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