308 research outputs found
Vegetation Detection and Classification for Power Line Monitoring
Electrical network maintenance inspections must be regularly executed, to provide
a continuous distribution of electricity. In forested countries, the electrical network is
mostly located within the forest. For this reason, during these inspections, it is also
necessary to assure that vegetation growing close to the power line does not potentially
endanger it, provoking forest fires or power outages.
Several remote sensing techniques have been studied in the last years to replace the
labor-intensive and costly traditional approaches, be it field based or airborne surveillance.
Besides the previously mentioned disadvantages, these approaches are also prone to
error, since they are dependent of a human operator’s interpretation. In recent years,
Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under
debate, due to its flexibility and potential for customisation, as well as the fact it can fly
close to the power lines.
The present study proposes a vegetation management and power line monitoring
method, using a UAV platform. This method starts with the collection of point cloud data
in a forest environment composed of power line structures and vegetation growing close
to it. Following this process, multiple steps are taken, including: detection of objects in
the working environment; classification of said objects into their respective class labels
using a feature-based classifier, either vegetation or power line structures; optimisation
of the classification results using point cloud filtering or segmentation algorithms. The
method is tested using both synthetic and real data of forested areas containing power line
structures. The Overall Accuracy of the classification process is about 87% and 97-99%
for synthetic and real data, respectively. After the optimisation process, these values were
refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison
and discussion of results is presented, providing the most important evaluation metrics
and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar
uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a
rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso,
durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede
elétrica não a coloca em risco, provocando incêndios ou falhas elétricas.
Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir
as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de
vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas
abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um
operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial
Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização,
assim como o facto de conseguirem voar mais próximas das linhas elétricas.
O presente estudo propõe um método para a gestão da vegetação e monitorização da
rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados
point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação
em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo:
deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas
de classe através de um classificador baseado em features, vegetação ou estruturas da rede
elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou
segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas
florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87%
e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização,
estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados
reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas
de avaliação mais importantes e uma representação visual dos resultados obtidos
Automatic vision based fault detection on electricity transmission components using very highresolution
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations.
Fault identification is one of the most significant bottlenecks faced by Electricity transmission and
distribution utilities in developing countries to deliver credible services to customers and ensure
proper asset audit and management for network optimization and load forecasting. This is due to
data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and
general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial
resolution to monitor four major Electric power transmission network (EPTN) components
condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks
(CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage
object detection model on the electric transmission power line imagery to localize, classify
and inspect faults present. The components fault considered include the broken insulator plate,
missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based
on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth
to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation
performed the best with a mean Average Precision of 89.61%. All the developed SSD based
models achieve a high precision rate and low recall rate in detecting the faulty components, thus
achieving acceptable balance levels F1-score and representation. Finally, comparable to other
works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection
and their component fault mapping in the long - run if these deep learning architectures are widely
understood, adequate training samples exist to represent multiple fault characteristics; and the
effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale
datasets are clearly understood
PAN-SUNET: UTILITY CORRIDOR UNDERSTANDING USING SPATIAL LAYOUT CONSISTENCY
The article addresses the need for a dependable and efficient computer vision system to examine utility networks with minimal human intervention, given the deteriorating state of these networks. To classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system, which is used for data collection, we suggest a deep learning network named Panoptic-Semantic Utility Network (Pan-SUNet). The proposed network incorporates three networks to achieve voxel-based semantic segmentation and 3D object detection of the point clouds at various resolutions, including object categories in three dimensions, and predicts two-dimensional regional labels to differentiate utility and corridor regions from non-corridor regions. The network also ensures spatial layout consistency in the prediction of the voxel-based 3D network using regional segmentation. By testing the proposed approach on 67 km2 of utility corridor data with an average density of 5 pts/m2, the paper demonstrates the effectiveness of the technique. The proposed network outperforms the state-of-the-art baseline network, achieving an F1 score of 94% for the pylon class, 99% for the ground class, 99% for the vegetation class, and 99% for the powerline class. It also shows high performance for 3D object detection for pylon and span achieving average precision of 99% and 92% respectively
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid
LiDAR is currently one of the most utilized sensors to effectively monitor
the status of power lines and facilitate the inspection of remote power
distribution networks and related infrastructures. To ensure the safe operation
of the smart grid, various remote data acquisition strategies, such as Airborne
Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser
Scanning (TSL) have been leveraged to allow continuous monitoring of regional
power networks, which are typically surrounded by dense vegetation. In this
article, an unsupervised Machine Learning (ML) framework is proposed, to
detect, extract and analyze the characteristics of power lines of both high and
low voltage, as well as the surrounding vegetation in a Power Line Corridor
(PLC) solely from LiDAR data. Initially, the proposed approach eliminates the
ground points from higher elevation points based on statistical analysis that
applies density criteria and histogram thresholding. After denoising and
transforming of the remaining candidate points by applying Principle Component
Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a
two-stage DBSCAN clustering to identify each power line individually. Finally,
all high elevation points in the PLC are identified based on their distance to
the newly segmented power lines. Conducted experiments illustrate that the
proposed framework is an agnostic method that can efficiently detect the power
lines and perform PLC-based hazard analysis.Comment: Accepted in the 22nd World Congress of the International Federation
of Automatic Control [IFAC WC 2023
Cooperative UAV–UGV autonomous power pylon inspection: an investigation of cooperative outdoor vehicle positioning architecture
Realizing autonomous inspection, such as that of power distribution lines, through unmanned
aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous
and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy
and safety of the operation; however, many technical problems, such as those pertaining to the precise
positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be
addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an
unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed
strategy, each vehicle provides its position information to the other, which ensures a safe inspection
process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating
the feasibility of the proposed approach.This research was funded by National Counsel of Technological and Scientific Development of Brazil (CNPq).
The authors thank the National Counsel of Technological and Scientific Development of Brazil
(CNPq); Coordination for the Improvement of Higher Level People (CAPES); and the Brazilian Ministry of Science,
Technology, Innovation, and Communication (MCTIC). The authors would also like express their deepest gratitude
to Control Robotics for sharing the Pioneer P3 robot for the experiments. Thanks to Leticia Cantieri for editing the
experiment video.info:eu-repo/semantics/publishedVersio
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Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data
Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud
Visual localisation of electricity pylons for power line inspection
Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot
Research and Technology Objectives and Plans Summary (RTOPS)
A compilation of summary portions of each of the Research and Technology Objectives and Plans (RTOPS) used for management review and control of research currently in progress throughout NASA is presented. Subject, technical monitors, responsible NASA organization, and RTOP number indexes are included
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