Live Line Snow and Ice Coverage Detection of Ceramic Insulator Using Gabor Image Features

Pernebayeva, Damira; James, Alex Pappachen; Bagheri, Mehdi

doi:10.1049/cp.2017.0340

Cited by 5 publications

(5 citation statements)

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“…8 Automatic patrol detection approaches based on airborne crafts and computer-aided techniques can be categorized into three groups: image processing-based (IP) methods, 9 machine learning-based (ML) methods, 10 and deep learning-based (DL) methods. 11 IP-based methods use segmentation and feature extraction techniques such as SIFT, 12 HOG, 13 Gabor, 14 edge detection, 15 morphology, 16 or wavelet 17 to separate targets from complex backgrounds in aerial images. However, this process is intricate and requires further accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

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MI-YOLO: more information based YOLO for insulator defect detection

Luan

et al. 2023

J. Electron. Imag.

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Insulators that connect high-voltage transmission lines may experience various faults due to long-term exposure to the natural environment, leading to safety degradation and reliability issues in power grids. Therefore, detecting defective insulators through daily maintenance and long-term overhaul is crucial. We propose an insulator defect detection method to achieve this objective, using a novel neural network named more information-you only look once (MI-YOLO). MI-YOLO includes several modifications compared to YOLOv5s: a skip connection module with downsampling in the backbone, a spatial pyramid dilated convolution module in the neck, and a novel serial-parallel spatial-channel attention module in between. These changes enhance the feature abstraction process by providing more information (MI) through the YOLO-like structure, hence the name MI-YOLO. To evaluate the superiority of MI-YOLO, multiple experiments are performed. First, an ablation study demonstrates the effectiveness of novel substructures and their combinations compared to YOLOv5s. Then, MI-YOLO is compared to baseline structures, including faster R-CNN, SSD, and five YOLOs. Furthermore, the performances of MI-YOLO and 10 state-of-the-art object detection methods are evaluated. Lastly, a second dataset is adopted to evaluate the generalization property and heat maps are presented. The experimental results show that the new designs substantially improve the accuracy of insulator fault detection by abstracting more information from the features.

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Section: Introductionmentioning

confidence: 99%

“…IP-based methods use segmentation and feature extraction techniques such as SIFT, 12 HOG, 13 Gabor, 14 edge detection, 15 morphology, 16 or wavelet 17 to separate targets from complex backgrounds in aerial images. However, this process is intricate and requires further accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

MI-YOLO: more information based YOLO for insulator defect detection

Luan

et al. 2023

J. Electron. Imag.

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“…The saliency analysis algorithm is applied to the extract region of the insulator ice layer and the length of the insulator icicle was calculated by the Fourier transform of the pixel distribution curve [22]. Pernebayeva et al studied a Gabor filtering algorithm for extracting a set of Gabor phase congruency features from insulator images for the presence or absence of snow, ice, and water droplets by utilizing the minimum distance nearest neighbor classifier [23]. Vita, V. et al constructed different neural network models for insulator contamination identification using different structures, learning algorithms, and transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree

Liu

Farzaneh

et al. 2020

Energies

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Serious ice accretion will cause structural problems and ice flashover accidents, which result in outdoor insulator string operating problems in winter conditions. Previous investigations have revealed that the thicker and longer insulators are covered with ice, the icing degree becomes worse and icing accident probability increases. Therefore, an image processing method was proposed to extract the characteristics of the icicle length and Rg (ratio of the air gap length to the insulator length) of ice-covered insulators for monitoring the operation of iced outdoor insulator strings. The tests were conducted at the artificial climate room of CIGELE Laboratories recommended by IEEE Standard 1783/2009. The surface phenomena of the insulator during the ice accretion process were recorded by using a high-speed video camera. In the view of the ice in the background of the picture of fuzzy features and high image noise, a direct equalization algorithm is used to enhance the grayscale iced image contrast. The median filtering method is conducted for reducing image noise and sharpening the image edge. The maximum entropy threshold segmentation algorithm is put forward to extract the insulators and its surface ice from the background. Then, the modified Canny operator edge detection algorithm is selected to trace the boundaries of objects through the extraction of information about attributes of the endpoints of edges. After we obtained the improved Canny edge detection image for both of the ice-covered insulators and non-iced insulators, the icing thickness can be obtained by calculating the difference between the edge of the non-iced insulators image and the edge of the iced insulator image. Besides, in order to identify the icing degree of the insulators more accurately, this paper determines the location of icicles by using the region growth method. After that, the icicle length and Rg can be obtained to monitor the icing degree of the insulator. It will be helpful to improve the ability to judge the accident risk of insulators in power systems.

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“…The on-line monitoring and identification technology of icing growth on artificial buildings have been studied for more than ten years, and many effective methods have been proposed, such as tension sensor measurement method [18], [19], image monitoring method [7], [20]- [22], capacitive sensor measurement method [23], [24], and so on. The image monitoring method is one of the most widely used methods at present, which can analyze the icing growth on power insulators through different indicators.…”

Section: Introductionmentioning

confidence: 99%

“…The image monitoring method is one of the most widely used methods at present, which can analyze the icing growth on power insulators through different indicators. Based on a large number pictures of on-site iced insulator, the image processing algorithms have been extensively studied, such as image denoting, image segmentation, boundary extraction and so on [7], [20]- [22]. The research mentioned above is mainly to evaluate the overall state of iced insulators, which is effective for rime icing and wet snow, because the volume of iced insulator has a significant change after icing accumulation.…”

Section: Introductionmentioning

confidence: 99%

Research on Image Recognition Method of Icicle Length and Bridging State on Power Insulators

et al. 2019

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Icicle will cause serious distortion of insulator electric field, weaken its voltage strength, and easily cause insulator flashover. Previous studies have shown that the longer the length of icicles on insulator, the greater the possibility of electric flashover. Then, it is of great theoretical significance and practical value to study the image identification technology of icicle length of iced insulators. Firstly, the growth mechanism and structural characteristics of insulator icicles are discussed in this paper, and the image acquisition method of iced insulator is discussed. Secondly, the methods of saliency detection and color feature analysis are used to extract the region of icicle and insulator sheds respectively, then, the icicle structure on insulator is obtained by the difference calculation of above two images. Meanwhile, the pixel curve of the iced insulator image is processed by Fourier transform, and the reference size of insulators icicle is obtained by calculating the length of the shed spacing. Finally, a practical case about iced insulators in substations is analyzed in detail, which proves the effectiveness of the method proposed in this paper.

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Live Line Snow and Ice Coverage Detection of Ceramic Insulator Using Gabor Image Features

Cited by 5 publications

References 0 publications

MI-YOLO: more information based YOLO for insulator defect detection

MI-YOLO: more information based YOLO for insulator defect detection

Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree

Research on Image Recognition Method of Icicle Length and Bridging State on Power Insulators

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