Abstract
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.
Highlights
Power lines are a typical part of urban and rural landscapes
The data collected and pre-processed from Datasets III and IV enabled the assessment of the accuracy of the 3D reconstruction of the power lines using unmanned aerial vehicle (UAV) imagery
This paper presents a comprehensive method of processing UAV images to detect and reconstruct 3D poTwhiesr lpinaepse,rthpernessuenbstseqauecnotlmypcroemhpenarseivtehemmewthitohda opfoipnrtoccloeussdinrgeprUeAseVntaitmioangoesf DtoSMdteotelcotcaalinzde arencyoonbstjeructcst t3hDreaptoewnienrglitnheess,atfheetny osuf bthseeqpuoewnetlrylicnoems.pare them with a point cloud representation of DSMPtoowloecralliinzee iannsypeocbtjieocntss athrereaattoenpiincagl tihsseuseaftehteyseofdtahyes.pMowoedrelliinngesw. ires in 3D space is essential for the assessment of power line safety
Summary
Due to the need for power, national and regional networks cover most of the world and continue to expand. They require regular monitoring and maintenance work. Monitoring power lines features two aspects: power line components and occlusions of the line corridor. Both are important and interconnected and are often addressed simultaneously. There are monitoring methods that are used to identify branches or canopies that endanger the inviolability of the power line corridor [2] or detect and classify trees to help evaluate the impact on the line [3]. Other techniques focus on volumetric analysis in order to evaluate the impact of the vegetation and its progression by calculating a differential map of a digital surface model (DSM) for two epochs [4]
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