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

Prasad, P. Surya; Rao, B. Prabhakara; Ece, Ucek

doi:10.25103/jestr.095.21

Cited by 26 publications

(12 citation statements)

References 47 publications

(71 reference statements)

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“…A few review articles discussed the specific application of power line inspection, which focused on one kind of the component or fault. Ahmad et al [11] proposed a review of advantages and limitations related to the vegetation encroachment monitoring of power lines Prasad et al [12] discussed the vision-based techniques for insulator monitoring of power lines. With the development of sensor technique, a number of remotely sensed data sources were applied in power line inspection.…”

Section: B Relevant Review Articlesmentioning

confidence: 99%

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

Liu,

Miao,

Jiang

et al. 2020

Preprint

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The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of power lines inspection data. With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and method performance. Further, an in-depth discussion of existing deep-learning-related analysis methods in power lines inspection is proposed. Finally, we conclude the paper with several research trends for the future of this area, such as data quality problems, small object detection, embedded application, and evaluation baseline.

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Section: B Relevant Review Articlesmentioning

confidence: 99%

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

Liu,

Miao,

Jiang

et al. 2020

Preprint

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“…However, the traditional manual method is inefficient and not feasible in practice as high-voltage transmission lines are usually built in complex surroundings containing forests and lakes. Recently, with the development of the unmanned aerial vehicle (UAV) control and image processing technique, insulator status inspection has trended towards the analysis of aerial images captured by UAVs [13]. The existing methods can be generalized into three main categories: (1) Man-made features-based methods, (2) machine learning-based methods, and (3) deep learning-based methods.…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

et al. 2019

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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“…Consequently, insulator detection based on computer vision is of great practical significance [3]. Over the past few years, the development of Unmanned Aerial Vehicle (UAV) and sensor techniques [4,5] has led to their exploitation as effective tools for transmission line inspection. Insulator detection by UAVs has become one of the primary research directions for intelligent grid systems [6].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Liu

et al. 2021

Electronics

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Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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

Cited by 26 publications

References 47 publications

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

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