Vision based inspection of transmission lines using unmanned aerial vehicles

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

doi:10.1109/mfi.2016.7849523

Cited by 24 publications

(8 citation statements)

References 21 publications

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“…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

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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.

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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

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“…Qin et al (2018) based their approach on a cable inspection robot to improve the payload and power capabilities of their inspection platform. Menendez et al (2016) presented a simulation of a UAV-based line tracking system and mounted a visual sensor on a robotic arm that detected and tracked lines.…”

Section: Detection Of Conductorsmentioning

confidence: 99%

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Odo

McKenna

Flynn

et al. 2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Visual inspection of electricity transmission and distribution networks relies on flying a helicopter around energized high voltage towers for image collection. The sensed data is taken offline and screened by skilled personnel for faults. This poses high risk to the pilot and crew and is highly expensive and inefficient. This paper reviews work targeted at detecting components of electricity transmission and distribution lines with attention to unmanned aerial vehicle (UAV) platforms. The potential of deep learning as the backbone of image data analysis was explored. For this, we used a new dataset of high resolution aerial images of medium-to-low voltage electricity towers. We demonstrated that reliable classification of towers is feasible using deep learning methods with very good results.

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“…In [10,14,[17][18][19] visual servoing type tracking [25] is used for UAV control, the method used is edge gradient detection. A smoothed image is filtered with the Sobel filter core [26] in the horizontal (Gx) and vertical (Gy) direction to obtain the first derivative (2) (3), then it traverses the pixels with a gradient vector that calculates the color intensity to identify the edge, as shown in Figure 5.…”

Section: Figure 2 Selection Process Of Primary Studiesmentioning

confidence: 99%

“…Canny filter gradient Some articles use additional methods to improve the accuracy or performance of the algorithms. In [10,17] a robust Hough transformation method [27] is used to reduce noise and identify geometric shapes in images such as lines. In [10,14] also uses region of interest (ROI) separation algorithms to reduce the edge of the image on a given object, eliminating parts that should not be processed.…”

Section: Figure 2 Selection Process Of Primary Studiesmentioning

confidence: 99%

Tracking for inspection in energy transmission power lines using unmanned aerial vehicles: a systematic review of current and specific literature

Martins

Filho

Jesus

et al. 2020

IJRA

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<p>Power transmission lines are of great importance for the operation of all sectors of society, such as commerce, industry and public agencies. To ensure reliability and availability of power supply, regular and occasional inspections are conducted, mostly using patrol with binoculars, helicopters or truck cranes. Research is being developed using unmanned aerial vehicle (UAV) to make this activity autonomous, faster, safer, and less costly. The present work aims to analyze research related to the autonomous control of the UAV along the transmission lines through a systematic review of the literature (SRL), apply a viable solution and to verify the possible lacuna in this state of the art. Improvements in safety, computational process and energy efficiency with low-cost were identified. The results presented can help the research community to perform the working this state of art, from the suggestions of autonomous tracking of transmission lines.</p>

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Vision based inspection of transmission lines using unmanned aerial vehicles

Cited by 24 publications

References 21 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

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Tracking for inspection in energy transmission power lines using unmanned aerial vehicles: a systematic review of current and specific literature

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