![]() ![]() However, it is still a challenge to automatically detect power lines with high precision. POWER DATA EXTRACTOR MANUALLight detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. Power-line inspection is an important means to maintain the safety of power networks. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires. Moreover, the planimetric accuracy for the detected pylons was 0.10 m. 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). It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. The seed regions thereafter are grown to extract the complete pylons. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. ![]() The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. POWER DATA EXTRACTOR SERIESThe series of hulls are then projected onto a horizontal plane to form individual corridors. 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. ![]() 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. 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. 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. 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. High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). ![]()
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