Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Menéndez, Oswaldo; Pérez, Marcelo A.; Cheein, Fernando Auat

doi:10.3390/app9010165

Cited by 41 publications

(17 citation statements)

References 59 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the dramatic increase of sensing data, it becomes more and more desirable to make image online monitoring. [8], [26], [27] develop effective methods to detect power lines based on the images captured by automated UAV (unmanned aerial vehicle). Besides, [28] builds a model to select the best thresholds for changing scenarios to detect and track power lines when using the unmanned aerial vehicle inspection, which is proved to be robust to the complexity of the real world.…”

Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

View full text Add to dashboard Cite

Image online monitoring technology has been widely used in transmission lines inspection, but the intelligent and efficient foreign object detection still has a gap with the ideal. In this paper, we propose a deep learning method to detect invading foreign objects for power transmission line inspection. Specifically, we design our network based on the regression strategy with oriented bounding boxes to accurately predict spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds. Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining procedure and boosting detection performance even with limited annotated samples. Besides, we construct an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average Precision (mAP). Additionally, the efficient and compact network structure allows our network to run in real-time, which provides possibilities for practical use. INDEX TERMS Image online monitoring technology, power transmission line inspection, deep learning, oriented bounding boxes, scale histogram matching.

show abstract

Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

View full text Add to dashboard Cite

show abstract

“…ROC of the classifier. (1) in the case of a 100% recall. The threshold points of the classifier are marked with triangles, indicating that all images with foreign objects can be screened when the images are classified by the threshold corresponding to the points.…”

Section: Classification Performance Evaluationmentioning

confidence: 99%

“…The main challenges of using drones for UAV (Unmanned Aerial Vehicles) maintenance are automatic pilot, flight time and communication bandwidth. The authors in [1] aim at solving automatic transmission line tracking problems. The authors in [2,3] face path planning and routing challenges when UAVs are flying along power transmission lines.…”

Section: Introductionmentioning

confidence: 99%

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

et al. 2019

View full text Add to dashboard Cite

The global demand for electric power has been greatly increasing because of industrial development and the change in people’s daily life. A lot of overhead transmission lines have been installed to provide reliable power across long distancess. Therefore, research on overhead transmission lines inspection is very important for preventing sudden wide-area outages. In this paper, we propose an Overhead Transmission Line Classifier (OTL-Classifier) based on deep learning techniques to classify images returned by future unmanned maintenance drones or robots. In the proposed model, a binary classifier based on Inception architecture is incorporated with an auxiliary marker algorithm based on ResNet and Faster-RCNN(Faster Regions with Convolutional Neural Networks features). The binary classifier defines images with foreign objects such as balloons and kites as abnormal class, regardless the type, size, and number of the foreign objects in a single image. The auxiliary marker algorithm marks foreign objects in abnormal images, in order to provide additional help for quick location of hidden foreign objects. Our OTL-Classifier model achieves a recall rate of 95% and an error rate of 10.7% in the normal mode, and a recall rate of 100% and an error rate of 35.9% in the Warning–Review mode.

show abstract

“…However, even if a good detection rate is obtained, that does not have necessary precision to carry out manipulation tasks. Nevertheless, there is a great amount of surveys for the interest of the reader related to this topic [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

POSITRON: lightweight active positioning compliant joints robotic arm in power lines inspection

Perez-Jimenez

Montes-Grova

Soria

et al. 2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

This paper presents the design and implementation of a compliant lightweight manipulator with an special end-effector to attach to power-lines. The manipulator can be mounted in aerial robots allowing to compute its relative position from the contact point. The purpose of this device is to obtain an estimate of the UAV's position to close the control loop. Controlling the position of the UAV close to the powerline enables a new wide range of inspection and maintenance tasks in this infrastructure. The article describes the model of the positioning tool and the sensors it uses to provide the necessary information for the UAV controller. It can be built using additive manufacturing techniques and its components are low-cost and available in common robotic stores so anyone can reproduce and use it. Validation experiments have been carried out in an Optitrack system as ground-truth.

show abstract

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Cited by 41 publications

References 59 publications

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

POSITRON: lightweight active positioning compliant joints robotic arm in power lines inspection

Contact Info

Product

Resources

About