Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles

Song, Jiang; Qian, J.; Li, Yongrong; Liu, Zhengjun; Chen, Yiming; Chen, Jianchang

doi:10.3390/s22176431

Cited by 10 publications

(5 citation statements)

References 66 publications

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“…A comparison with previously published carbonaceous materials as catalysts for the same transformations shows that C8LZ presents the highest v max and the secondhighest K M reported to date (Table S7). [23,24] Due to the low catalytic activity of C5LZ, C6LZ, and C7LZ in both reactions, few conclusions can be drawn. Although electron-poor carbons are, to a certain degree, responsible for the adsorption of small molecules, their amount and Lewis acidity is likely not the critical parameter to promote these reactions.…”

Section: Resultsmentioning

confidence: 99%

Catalytic Properties of High Nitrogen Content Carbonaceous Materials

et al. 2022

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The influence of structural modifications on the catalytic activity of carbon materials is poorly understood. A collection of carbonaceous materials with different pore networks and high nitrogen content was characterized and used to catalyze four reactions to deduce structure-activity relationships. The CO 2 cycloaddition and Knoevenagel reaction depend on Lewis basic sites (electron-rich nitrogen species). The absence of large conjugated carbon domains resulting from the introduction of large amounts of nitrogen in the carbon network is responsible for poor redox activity, as observed through the catalytic reduction of nitrobenzene with hydrazine and the catalytic oxidation of 3,3',5,5'-tetramethylbenzidine using hydroperoxide. The material with the highest activity towards Lewis acid catalysis (in the hydrolysis of (dimethoxymethyl)benzene to benzaldehyde) is the most effective for small molecule activation and presents the highest concentration of electron-poor nitrogen species.

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Section: Resultsmentioning

confidence: 99%

Catalytic Properties of High Nitrogen Content Carbonaceous Materials

et al. 2022

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“…In this kind of framework, the detection of false positives should be minimized and additional post processing can be carried out for further refinement of the captured images. The data extracted from UAVs can be used to develop automated vision-based condition assessment techniques for power distribution lines by implementing learning algorithms [108][109][110]. Learning algorithms can be effective in preprocessing, analysis and post processing the data to draw conclusions about the overall condition.…”

Section: Advanced Inspection Techniques (Via Mobile Robots and Unmann...mentioning

confidence: 99%

“…[107] UAVs Implemented filtering techniques, morphological operations and different mathematical techniques to extract power lines from images suppressing the background [108][109][110] UAVs and deep learning techniques Employed convolutional neural networks and other deep learning approaches for automated analysis of the data obtained from UAVs. Learning algorithms provided effective preprocessing, analysis and post-processing.…”

Section: Studymentioning

confidence: 99%

Power Distribution System Faults and Wildfires: Mechanisms and Prevention

Bandara

Rajeev

Gad

2023

Forests

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Wildfires are one of the most hazardous natural disasters in Australia in terms of fatalities, property damage and financial losses. Events of catastrophic wildfires are recorded across the world including in the United States and Canada. Failures along power distribution infrastructure and network faults have been identified as some of the causes for the initiation of wildfires. Thus, it is critical to better understand the mechanisms and the potential prevention strategies for wildfires caused by power distribution system faults. In this light, this paper presents how the power distribution network faults cause wildfires highlighting the main mechanisms. Further, this paper reviewed studies on recent advancements for the prediction, detection and prevention of wildfires. Condition assessment of power distribution infrastructure including poles, crossarms, overhead cables and other attachments are paramount to detect potential defects and to carry out timely replacements which can subsequently mitigate the possibility of wildfire initiation. Therefore, this paper summarized the studies on condition monitoring and surveillance techniques for power distribution infrastructure. Altogether, this paper aimed to enhance the awareness about the prevention strategies for wildfires caused by power distribution system faults.

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“…Despite the importance of monitoring vegetation encroachment in transmission lines, the most used technique is human-based field trips, known to be a time-consuming task and logistically intensive due to the long journeys and harsh natural conditions that must be faced. Consequently, this technique suffers from poor scalability, as reported in [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, refs. [2][3][4][5][6] proposed applying deep learning combined with Unmanned Aerial Vehicle (UAV) images of transmission lines. Furthermore, optical imagery captured by UAVs are easy to collect, analyze and store.…”

Section: Introductionmentioning

confidence: 99%

VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery

Cano-Solis,

Ballesteros,

Sanchez-Torres

2023

IJGI

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Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply.

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Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles

Cited by 10 publications

References 66 publications

Catalytic Properties of High Nitrogen Content Carbonaceous Materials

Catalytic Properties of High Nitrogen Content Carbonaceous Materials

Power Distribution System Faults and Wildfires: Mechanisms and Prevention

VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery

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