Power line inspection via an unmanned aerial system based on the quadrotor helicopter

Luque-Vega, Luis F.; Castillo–Toledo, B.; Loukianov, Alexander G.; Gonzalez-Jimenez, Luis Enrique

doi:10.1109/melcon.2014.6820566

Cited by 149 publications

(75 citation statements)

References 12 publications

(11 reference statements)

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“…In [87], the authors proposed for the deployment of UAV with a vision-based system that consists of a color camera, TIR camera and a transmitter to send the captured images to the GCS. Then these images will be processed in the GCS, to be used in the inspection and estimation of the real temperature of the power lines joints.…”

Section: Research Trends and Future Insightsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

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Section: Research Trends and Future Insightsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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“…To the best of our knowledge and disregarding patents [26,27], this is the first industrial-scale application of aerial thermography for rollers' inspection. Other works linking thermography and UAV are focused on diverse applications, like crop management [29,30], power line [31,32] monitoring, and solar power plants inspection [33][34][35]. The works of [36,37] are examples of applications that use drones and machine learning algorithms to aid inspection processes.…”

Section: Condition Monitoring Techniquesmentioning

confidence: 99%

A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry

Carvalho

Nascimento

D'Angelo

et al. 2020

Sensors

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Frequent and accurate inspections of industrial components and equipment are essential because failures can cause unscheduled downtimes, massive material, and financial losses or even endanger workers. In the mining industry, belt idlers or rollers are examples of such critical components. Although there are many precise laboratory techniques to assess the condition of a roller, companies still have trouble implementing a reliable and scalable procedure to inspect their field assets. This article enumerates and discusses the existing roller inspection techniques and presents a novel approach based on an Unmanned Aerial Vehicle (UAV) integrated with a thermal imaging camera. Our preliminary results indicate that using a signal processing technique, we are able to identify roller failures automatically. We also proposed and implemented a back-end platform that enables field and cloud connectivity with enterprise systems. Finally, we have also cataloged the anomalies detected during the extensive field tests in order to build a structured dataset that will allow for future experimentation.

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“…To that end, helicopter inspection can be utilised [9]. The inspection can then be performed online or offline.…”

Section: Electrical Distribution Pole Inspectionmentioning

confidence: 99%

Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks

Liu

Zhang

Zhao

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Electrical distribution poles are important assets in electricity supply. These poles need to be maintained in good condition to ensure they protect community safety, maintain reliability of supply, and meet legislative obligations. However, maintaining such a large volumes of assets is an expensive and challenging task. To address this, recent approaches utilise imagery data captured from helicopter and/or drone inspections. Whilst reducing the cost for manual inspection, manual analysis on each image is still required. As such, several image-based automated inspection systems have been proposed. In this paper, we target two major challenges: tiny object detection and extremely imbalanced datasets, which currently hinder the wide deployment of the automatic inspection. We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest. To address the imbalanced dataset problem, we propose the resampling as well as reweighting schemes to iteratively adapt the model to the large intra-class variation of major class and balance the contributions to the loss from each class. Finally, we integrate these components together and devise a novel automatic inspection framework. Extensive experiments demonstrate that our proposed approaches are effective and can boost the performance compared to the baseline methods.

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Power line inspection via an unmanned aerial system based on the quadrotor helicopter

Cited by 149 publications

References 12 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry

Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks

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