Advanced Power Line Diagnostics Using Point Cloud Data—Possible Applications and Limits

Siranec, Marek; Höger, Marek; Otcenasova, Alena

doi:10.3390/rs13101880

Cited by 12 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A point cloud can directly describe three-dimensional objects in real life, and it is widely used in industries such as automatic driving [1], disaster warning [2], and power diagnosis [3]. However, point clouds are derived from nature, so they are inevitably disturbed by some noise generated by laser scanning, shading, or wind and object vibration [4].…”

Section: Introductionmentioning

confidence: 99%

Nonparametric point cloud filter

Sun,

Wang

2023

IET Image Processing

View full text Add to dashboard Cite

This paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

Nonparametric point cloud filter

Sun,

Wang

2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Making airborne LiDAR unmanned greatly reduces the costs (equipment cost and data acquisition cost) and improves the efficiency, which we term UAV-based LiDAR in this paper. It has been widely used in topographic surveys [29], power line diagnoses [30], dam deformation monitoring [31], vegetation height measurements [32,33], and so on. However, few scholars have applied UAV-based LiDAR to detecting the subsidence of the working face of a mining area.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR

et al. 2022

View full text Add to dashboard Cite

Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment.

show abstract

“…In addition to traditional applications in the remote sensing and photogrammetry communities (Zhang and Lin, 2017), three‐dimensional (3D) ALS point clouds have drawn significant attention from many other domains as well. For instance, high‐definition map creation for autonomous driving (Liu et al., 2020), estimation of tree‐level forest biomass (Wang et al., 2019c), power line diagnostics (Siranec et al., 2021), digital protection of cultural heritage (Ulvi, 2021), and 3D urban building reconstruction (Zhang et al., 2018). 3D building models can further help estimate the solar energy potential in urban areas (Nelson and Grubesic, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi‐Task Pointwise Network for Segmentation of Building Roofs in Airborne Laser Scanning Point Clouds

Zhang

Fan

2022

The Photogrammetric Record

View full text Add to dashboard Cite

Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data-sets or they are not automatic and efficient. To tackle these issues, an improved multi-task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean-shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data-set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data-set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task.

show abstract

Advanced Power Line Diagnostics Using Point Cloud Data—Possible Applications and Limits

Cited by 12 publications

References 37 publications

Nonparametric point cloud filter

Nonparametric point cloud filter

An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR

An Improved Multi‐Task Pointwise Network for Segmentation of Building Roofs in Airborne Laser Scanning Point Clouds

Contact Info

Product

Resources

About