3D Power Line Extraction from Multiple Aerial Images

The maintenance of high-voltage power lines rights-of-way due to vegetation intrusions is important for electric power distribution companies for safe and secure delivery of electricity. However, the monitoring becomes more challenging if power line corridor (PLC) exists in complex environment such as mountainous terrains or forests. To overcome these challenges, this paper aims to provide an automated method for extraction of individual pylons and monitoring of vegetation near the PLC in hilly terrain. The proposed method starts off by dividing the large dataset into small manageable datasets. A voxel grid is formed on each dataset to separate power lines from pylons and vegetation. The power line points are converted into a binary image to get the individual spans. These span points are used to find nearby vegetation and pylon points and individual pylons and vegetation are further separated using a statistical analysis. Finally, the height and location of extracted vegetation with reference to power lines are estimated and separated into danger and clearance zones. The experiment on two large Australian datasets shows that the proposed method provides high completeness and correctness of 96.5% and 99% for pylons, respectively. Moreover, the growing vegetation beneath and around the PLC that can harm the power lines is identified.

Remote sensing methods for power line corridor surveys

Power line mapping technique using all-terrain mobile laser scanning

A power line segmentation model in aerial images based on an efficient multibranch concatenation network

Maintaining and monitoring low-voltage overhead power lines are of great importance because such lines are the key link between the power system and low-voltage power users. At present, few networks can be detected accurately on intelligent edge identification devices because of the complex backgrounds and limited characteristics in unmanned aerial vehicle images as well as the low computing abilities of hardware. In order to give consideration to accuracy and speed, a novel power line detection method was proposed, denoted by Gabor-YOLONet, used for intelligent edge identification devices available to UAV. Unlike existing methods, the proposed method uses the Gabor algorithm to extract foreground of power lines from cluttered backgrounds automatically and predict power lines and their auxiliary targets such as insulators in the foreground scene. In addition, a new inference method was introduced, which can summarize the average location and orientation of auxiliary targets by clustering to verify the rationality of the predicted results for power lines. The experiment results showed that the proposed method had the higher accuracy and consumed less computing resources; compared with other methods, the mAP of identification for power lines was 86.6% and the running time was only 25 ms, with excellent performance on intelligent edge devices.

Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power system.The aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.

Power lines (PLC), mainly dedicated and optimized for delivering electricity, are, nowadays, used to transfer data. Its low cost and wide coverage makes it one of the pivotal technologies in building up smart grid. The actual role of PLC technology is controversial while some preconize that PLC systems are very good candidates for some applications others discard it and look at wireless as a more elaborated alternative. It is obvious that Smart Grid will include multiple types of communications technologies ranging from optics to wireless and wireline. Among wireline solutions, PLC appears to be the only technology that has deployment cost comparable to wireless as power installation and line are already existent. Narrowband PLCs are a key point in smart grid that is elaborated to support several applications such as Automatic Meter Reading (AMR), Advanced Metering Infrastructure (AMI), demand side management, in-home energy management and Vehicle-to-grid communications A critical stage in designing an efficient PLC system remains in getting sufficient knowledge about channel behavior characteristics such as attenuation, access impedance, multiple noise scenarios and synchronization. That's why; characterizing power line network has been the interest of several research works aiming to compromise between robustness of powerline communication and higher data rate. This interest in narrowband PLC systems to find the adequate one is inciting to deeply focus on channel characterization and modeling methods. It represents a first step to simulate channel behavior then propose a stand-alone hardware for emulation. Authors are investigating the building blocks of a narrowband PLC channel emulator that helps designers to evaluate and verify their systems design. It allows a reproduction of real conditions for any narrowband PLC equipment (single carrier or multicarrier) by providing three major functionalities: noise scenarios, signal attenuation and zero crossing reference mainly used for single carriers systems. For this purpose, authors deploy a bottom up approach to identify a real channel transfer function (TF) based on a prior knowledge of used power cables characteristics and connected loads. A simulator is, then, defined based on Matlab that generates a given TF according to defined parameters. The AC mains zero crossing variation is also studied. In field exhaustive measurements of this reference have shown a perpetual fluctuation presented as a jitter error. It is the reflection of variant AC mains characteristics (frequency, amplitude) which could be related to non-linearity of connected loads at network and used detection circuit in PLC systems. Authors propose a ZC variation model according to system environment (home/ lab, rural). This model will be embedded on channel emulator to reproduce ZC reference variation. Regarding noise, few models are found in literature, specific to narrowband PLCs. An implementation of some models is done and tested on a DSP platform which will include the two previous elements: TF and ZC variation.

<p>After World War II, aerial photography i.e. vertical or oblique high-resolution aerial images spread rapidly into civil research sectors, such as landscape studies, geologic maps, natural sciences, archaeology, and more. Applying photogrammetric techniques, two or more overlapping historical aerial images can be used to generate an orthophoto and a 3D point cloud, wherefrom a digital elevation model can be derived for the respective epoch. Combining results from different epochs, morphological processes and elevation changes of the surface caused by anthropogenic and natural factors can be assessed. Despite the unequalled potential of such data, their use is not yet fully exploited. Indeed, there is a lack of clear processing workflows applying either traditional photogrammetric techniques or structure from motion (SfM) with camera self-calibration. In fact, on the one hand, many SfM and multi-view stereo software do not deal with scanned images. On the other hand, traditional photogrammetric approaches require information such as a camera calibration protocol with fiducial mark positions. Furthermore, the quality of the generated products is strongly affected by the quality of the scanned images, in terms of the conservation of the original film, scanner resolution, and acquisition parameters like image overlap and flying height.</p><p>To process a large dataset of historical images, an approach based on multi-epoch bundle adjustment has been suggested recently.  The idea is to jointly orient the images of all epochs of a historical image dataset. This recent approach relies on the robustness of the scale-invariant feature transform (SIFT) algorithm to automatically detect common features between images of the time series located in stable areas. However, this approach cannot be applied to process digital images of alpine environments, characterized by continuous changes also of small magnitude that might be challenging to automatically identify in image space. In this respect, our method implemented in OrientAL, a software developed by TU Wien, identifies stable areas in object space across the entire time series. After the joint orientation of the multi-epoch aerial images, dense image matching is performed independently for each epoch. We tested our method on an image block over the alpine catchment Kaunertal (Austria), captured at nine different epochs with a time span of fifty years. Our method definitely speeds up the process of image orientation of the entire data set, since stable areas do not need to be masked manually in each image. Furthermore, we could improve the orientation of images from epochs with poor overlap. To estimate the improvements obtained with our methods in terms of time and accuracy of the image orientation, we compare our results with photogrammetric and commercial SfM software and we analyse the accuracy of tie points with respect to a reference Lidar point cloud. The work is part of the SEHAG project (project number I 4062) funded by the DFG and FWF.</p>

To address the issues of noise interference,the class imbalance, and insufficient 3D reconstruction accuracy in airborne LiDAR power line point cloud extraction, this paper proposes a method for power line extraction and 3D reconstruction based on an improved PointNet++. Firstly, the dataset is divided, and then multi-dimensional features such as point cloud spatial information and geometric features are extracted through various means. Next, PointNet++ is improved by introducing local relative height features to enhance vertical dimension information. A three-level feature extraction network is constructed, and Focal Loss is adopted in the training stage to solve the class imbalance problem. Dynamic learning rate adjustment and early stopping mechanisms are implemented with power line IoU as the monitoring metric. Finally, the DBSCAN algorithm is used to fit the parametric equation of the power line, and continuous frame point cloud registration is combined to achieve 3D reconstruction of the power corridor. Experimental results show that on a power line point cloud dataset with 4096 points sampled, this method achieves a power line IoU of $\mathbf{9 4 . 9 \%}$ and an overall mIoU of $\mathbf{9 7 . 2 7 \%}$ on the validation set.

Recently, intelligent unmanned aerial vehicles (UAVs) have shown great advantages of flexibility and productivity in power line inspection, wherein robust detection of power lines from aerial images for automatic power line following navigation is required. However, identifying power lines accurately from a cluttered background is challenging due to the limited resolution of onboard cameras and the noisy environment. In this letter, we propose a novel power line detection method, denoted by CODNet, for the application of UAV navigation. Unlike existing works, the proposed method can extract features of power lines from cluttered backgrounds automatically and predict centers and orientations of power lines in the scene simultaneously. Besides, we introduce a new clustering method to summarize the average location and orientation of detected power lines as a guide for the automatic navigation of UAVs. Finally, experimental results demonstrate both the effectiveness and the superiority of the CODNet.

Power line segmentation is very important to ensure the safe and stable operation of unmanned aerial vehicles in intelligent power line inspection. Although the power line segmentation algorithm based on deep learning has made some progress, it is still quite difficult to achieve accurate power line segmentation due to the complex and changeable background of aerial power line images and the small power line targets, and the existing segmentation models is too large and not suitable for edge deployment. This paper proposes a lightweight power line segmentation algorithm—G-UNets. The algorithm uses the improved U-Net of Lei Yang et al. (2022) as the basic network (Y-UNet). The encoder part combines traditional convolution with Ghost bottleneck to extract features and adopts a multi-scale input fusion strategy to reduce information loss. While ensuring the segmentation accuracy, the amount of Y-UNet parameters is significantly reduced; Shuffle Attention (SA) with fewer parameters is introduced in the decoding stage to improve the model segmentation accuracy; at the same time, in order to further alleviate the impact of the imbalanced distribution of positive and negative samples on the segmentation accuracy, a weighted hybrid loss function fused with Focal loss and Dice loss is constructed. The experimental results show that the number of parameters of the G-UNets algorithm is only about 26.55% of that of Y-UNet, and the F1-Score and IoU values both surpass those of Y-UNet, reaching 89.24% and 82.98%, respectively. G-UNets can greatly reduce the number of network parameters while ensuring the accuracy of the model, providing an effective way for the power line segmentation algorithm to be applied to resource-constrained edge devices such as drones.

Continuous monitoring of the sag plays a crucial role in ensuring the safe operation of high voltage transmission lines and adjusting transmission efficiency. Current sag measurement technologies are insufficient for quick transmission line inspection. In this study, a self-developed sag measurement platform is proposed, along with a sag measurement model that incorporates spatial coordinate transformation. Furthermore, a transmission line sag measurement method based on an improved Least Median of Squares (LMedS) algorithm is introduced. This method utilizes data from a laser rangefinder and an angle sensor. First, M-estimation is applied to select the measurement points of the transmission line, preserving the effective features of the transmission line while reducing the interference of erroneous data points. Subsequently, an adaptive inertia weight strategy for Particle Swarm Optimization (PSO) is designed to enhance the convergence speed of LMedS in minimizing the squared median residuals, thereby accelerating the identification of the transmission line parabolic model. The average sag error rate of the algorithm in this study is 1.175%, which meets the standard requirements through simulation experiments and field experiments. It also improves the safety and efficiency of the sag measurement of overhead power lines.

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Automatic recognition of scenes with power line wires in real life aerial images using DCT-based features

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance.