Classifier-Free Detection of Power Line Pylons From Point Cloud
Data

Awrangjeb, Mohammad; Islam, Khaled Ben

doi:10.5194/isprs-annals-iv-4-w4-81-2017

Cited by 10 publications

(12 citation statements)

References 9 publications

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“…Pylon detection techniques that provide individual pylon locations were proposed by Sohn et al [6] and Awrangjeb and Islam [5]. Sohn et al [6] first extracted 3D lines using the RANSAC (RANdom SAmple Consensus) algorithm.…”

Section: Pylon Detectionmentioning

confidence: 99%

“…Finally, non-pylon objects were removed using a voting scheme based on contextual relations among the lines. Using the non-ground points, Awrangjeb and Islam [5] first generated a PL mask, where successive pylons were found connected to each other by wires. They also generated a pylon mask within a specific low-height region where pylons were not connected with wires at all.…”

Section: Pylon Detectionmentioning

confidence: 99%

“…Both methods have shown 100% success rate in the detection of pylons. However, the application of Awrangjeb and Islam [5] to a complex forest dataset revealed that the method is not effective in extraction of pylons, since a common low height region cannot be found in a hilly terrain. Moreover, when trees exist within a corridor and underneath the wires, long straight lines that represent wires cannot be extracted effectively.…”

Section: Pylon Detectionmentioning

confidence: 99%

“…However, when there are tall trees below the PLs, as can be seen in Figure 2b, both trees and wires exist as non-ground points. For instance, Figure 2c shows the PL mask at h = 1 m [5] indicating that vegetation exists in all three corridors, but especially in Corridor 3. Thus, a small value of h will not be effective to separate the expected objects from the unexpected objects.…”

Section: Mask and Convex Hull Generationmentioning

confidence: 99%

“…It should be noted that most of the existing PL extraction methods (e.g., McLaughlin [4]) overlook the detection of pylons as a pre-requisite for PL extraction, except that of Awrangjeb and Islam [5]. Sohn et al [6] detected individual pylons, but did not use their location to help with the extraction of points for individual wires.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels

Awrangjeb

2019

Remote Sensing

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High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.

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Section: Pylon Detectionmentioning

confidence: 99%

Section: Pylon Detectionmentioning

confidence: 99%

Section: Pylon Detectionmentioning

confidence: 99%

Section: Mask and Convex Hull Generationmentioning

confidence: 99%