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

Martins, Wander M.; Filho, Antonio J. Dantas; Jesus, Leandro Diniz de; Souza, Adler Diniz de; Ramos, Alexandre C. B.; Pimenta, Tales C.

doi:10.11591/ijra.v9i4.pp233-243

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Traditionally, power line inspections have been conducted manually by workers, assisted mainly by helicopters. These methods are not only costly but also time-consuming and expose workers to high risk [1]. As a result, researchers have shifted their focus towards automated, remote inspection solutions utilizing unmanned aerial vehicles (UAVs), or drones.…”

Section: Introductionmentioning

confidence: 99%

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Bellou,

Pisica,

Banitsas

2024

Energies

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The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8’s architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model’s pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5).

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Section: Introductionmentioning

confidence: 99%

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Bellou,

Pisica,

Banitsas

2024

Energies

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“…It may be sited within the organization or out of organization. Public cloud is an infrastructure which own by an organization selling cloud service for user or any public user and public cloud can be used for public business and so many other activity [9], [10]. Hybrid cloud comprise of two or more cloud computing it's may be private or public and used for different reason and public cloud use for less sensitive data and private used for more sensitive data.…”

Section: Introductionmentioning

confidence: 99%

CloudIoT paradigm acceptance for e-learning: analysis and future challenges

Ullah

Aznaoui²,

Şahin³

et al. 2022

Jurnal Informatika

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E-learning is the theme interrelated to the virtualized distance learning with the help of electronic communication machines, certainly with the help of Internet. CloudIoT paradigm is the combination of cloud resource and internet of thing which become prevalent now days due to the flexibility and fast access for those reason different countries used CloudIoT paradigm different purposes. E-learning is one of the best examples where virtual environment provides cost-effective alternative to physical labs as well as to run scientific applications. The world order change in education sector due to Covid-19 all activity shift in to e-learning system. In this paper we present the review about CloudIoT paradigm and it usage in e-learning system as well as we extant taxonomy of CloudIoT paradigm for e-leaning purpose. In the related work section we present the existing contribution in the field of elearning using CloudIoT paradigm are highlighted. We also contemporaneous the most standard framework which carried out for e-leaning using CloudIoT paradigm is discuss. The contribution section of the paper present the issue being faced by in adopting CloudIoT paradigm for e-learning are discussed along with recommendation and future work.

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Convolutional Neural Network Based Unmanned Ground Vehicle Control via Deep Reinforcement Learning

Liu

Wang

et al. 2022

2022 4th International Conference on Control and Robotics (ICCR)

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

Cited by 6 publications

References 25 publications

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

CloudIoT paradigm acceptance for e-learning: analysis and future challenges

Convolutional Neural Network Based Unmanned Ground Vehicle Control via Deep Reinforcement Learning

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