A method for detecting power lines in UAV aerial images

Liu, Xiong; Hou, Lin; Ju, Xiongming

doi:10.1109/compcomm.2017.8322913

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Considerable research has been dedicated to power line monitoring using UAVs. Most of it has focused solely on wire detection in images [26][27][28][29][30][31][32][33][34], but a few studies have taken a more holistic approach by considering not only the position of the power line, but also the line corridor and obstacles. The means of data processing and 3D power line reconstruction are different and depend on the aim of the calculation.…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

et al. 2020

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Regular power line inspections are essential to ensure the reliability of electricity supply. The inspections of overground power submission lines include corridor clearance monitoring and fault identification. The power lines corridor is a three-dimensional space around power cables defined by a set distance. Any obstacles breaching this space should be detected, as they potentially threaten the safety of the infrastructure. Corridor clearance monitoring is usually performed either by a labor-intensive total station survey (TS), terrestrial laser scanning (TLS), or expensive airborne laser scanning (ALS) from a plane or a helicopter. This paper proposes a method that uses unmanned aerial vehicle (UAV) images to monitor corridor clearance. To maintain the adequate accuracy of the relative position of wires in regard to surrounding obstacles, the same data were used both to reconstruct a point cloud representation of a digital surface model (DSM) and a 3D power line. The proposed algorithm detects power lines in a series of images using decorrelation stretch for initial image processing, the modified Prewitt filter for edge enhancement, random sample consensus (RANSAC) with additional parameters for line fitting, and epipolar geometry for 3D reconstruction. DSM points intruding into the corridor are then detected by calculating the spatial distance between a reconstructed power line and the DSM point cloud representation. Problematic objects are localized by segmenting points into voxels and then subsequent clusterization. The processing results were compared to the results of two verification methods—TS and TLS. The comparison results show that the proposed method can be used to survey power lines with an accuracy consistent with that of classical measurements.

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Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

et al. 2020

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“…Both in [19] and [20], pylon is used as an auxiliary object to assist the power line extraction. Liu et al [21] proposed an improved randomized Hough transform (RHT) to extract power lines. Cao et al [22] utilized Radon transform (RT) to extract power lines.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [23] used HT to extract line segments and utilized K-means in the Hough space to extract power lines. In [21], [22] and [23], power lines are regarded as parallel lines appearing in groups and they use this criterion as context-assisted information to extract power lines. Chen et al [24] used a cluster Radon transform (CRT) to extract line segments and distinguish between power lines and other line segments based on the criterion that the background on both sides of power line is more similar.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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The extraction of power line plays a key role in power line inspection by Unmanned Aerial Vehicles (UAVs). While it is challenging to extract power lines in aerial images because of the weak targets and the complex background. In this paper, a novel power line extraction method is proposed. First of all, we create a line segment candidate pool which contains power line segments and large amount of other line segments. Secondly, we construct the irregular graph model with these line segments as nodes. Then a novel object-based Markov random field with anisotropic weighted penalty (OMRF-AWP) method is proposed. It defines a new neighborhood system based on the irregular graph model and builds a new potential function by considering the region angle information. With the OMRF-AWP method, we can distinguish between the power line segments and other line segments. Finally, an envelope-based piecewise fitting (EPF) method is proposed to fit the power lines. Experimental results show that the proposed method has good performance in multiple scenes with complex background. INDEX TERMS Power line extraction, object-based Markov random field (OMRF), anisotropic weighted penalty, irregular graph model, potential function.

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