Towards autonomous detection and tracking of electric towers for aerial power line inspection

Martínez, Carol; Sampedro, Carlos; Chauhan, Aneesh; Campoy, Pascual

doi:10.1109/icuas.2014.6842267

Cited by 83 publications

(39 citation statements)

References 22 publications

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“…Dutta et al [47] have taken aerial imagery using Unmanned Aerial Vehicle (UAV) with varying natural and complex surroundings to detect line faults using a novel morphological operator, and a robust image space heuristics to locate and extract power lines completely. Martinez et al [48] presents an approach focusing on autonomous detection in real-time, electric towers localization and tracking using a strategy to train a two-class multilayer perceptron (MLP) neural network and applied over sliding windows for each camera frame until a tower is detected. Electric towers, as well as the insulators, are extracted by V.S.Murthy et al [36] by converting the color images first into gray scaled images and then Canny edge detection which is followed by the modified Hough transform.…”

Section: Spatial Domain Methodsmentioning

confidence: 99%

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Prasad¹,

Rao²,

Ece³

2016

JESTR

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In the present world, there is a great necessity to have reliable and quality power distribution, and so there is great scope for research on automation of distribution system. The main objective of this paper is to analyze and comprehend different machine learning and image processing based algorithms to find a practical solution for automated inspection of overhead power line insulators. This method is a relatively new approach for. This paper also highlights the constraints and limitations that are present in the various existing methodologies to achieve the objective. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. The few techniques like artificial neural networks (ANN), Hidden Markov Model (HMM), k-means clustering, Wavelet transform features, S-transform features, and support vector machines (SVM) applied in the domain of condition monitoring of the insulators were presented.

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Section: Spatial Domain Methodsmentioning

confidence: 99%

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Prasad¹,

Rao²,

Ece³

2016

JESTR

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show abstract

“…Primarily, as drone inspection is based on the images captured by the airborne camera, various classification and recognition algorithms are designed [26][27][28][29][30]. Martinez et al [26] combined classic computer vision and machine learning for autonomous aerial powerline inspection and localization. To distinguish the transmission line from the natural outdoor environment, a decomposition structure is used in transmission line detection (TLD) algorithm [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

et al. 2019

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A novel high-voltage powerline inspection system was investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle’ route in the road network and drone’s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new Two-Layer Point-Arc Routing Problem (2L-PA-RP). Two constructive heuristics were designed based on “Cluster First, Route Second” and “Route First, Split Second”. Then, local search strategies were developed to further improve the quality of the solution. To test the performance of the proposed algorithms, different-scale practical cases were designed based on the road network and powerline network of Ji’an, China. Sensitivity analysis on the parameters related to the drone’s inspection speed and battery capacity was conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.

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“…In order to ensure the accuracy of drone inspection, some algorithms were proposed. Primarily, a variety of classification and recognition algorithms were designed as the drone inspection is based on the images captured by the airborne camera [26][27][28][29][30]. The supervised learning approach and network training method were applied in the classification problem of inspection images [26,28].…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

Liu¹,

Shi²,

Liu³

et al. 2019

Preprint

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A novel high-voltage powerline inspection system is investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle’ route in the road network and drone’s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new two-layer point-arc routing problem. Two constructive heuristics are designed based on “Cluster First, Rank Second” and “Rank First, Split Second”. Then local search strategies are developed to further improve the quality of the solution. To test the performance of the proposed algorithms, practical cases with different-scale are designed based on the road network and powerline network of Ji’an, China. Sensitivity analysis on the parameters related with the drone’s inspection speed and battery capacity is conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.

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Towards autonomous detection and tracking of electric towers for aerial power line inspection

Cited by 83 publications

References 22 publications

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

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