Technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection

Yang, Luya; Huang, Xinbo; Zhang, Ye; Tang, Nujiang

doi:10.1109/access.2019.2954935

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Inspired by the two-person zero-sum game [9], the network consists of a generator network and a discriminator network and learns the data distribution through adversarial learning. e main purpose of the proposed GAN network is to estimate the potential distribution of data samples and generate a large number of new data samples.…”

Section: Related Workmentioning

confidence: 99%

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Wang¹,

Ma²

2022

Mathematical Problems in Engineering

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In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a “hole” and uses the idea of “hole filling” to eliminate the reflective point, which weakens the reflection problem to a certain extent.

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Section: Related Workmentioning

confidence: 99%

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Wang¹,

Ma²

2022

Mathematical Problems in Engineering

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“…When insulators have self-explosion defects, if they are not handled in a timely manner, they may lead to insulator fractures, impacting insulators such as towers, affecting the reliability of transmission lines, and even causing widespread power outages in the power grid, resulting in significant economic losses [ 1 ]. Therefore, the timely detection and accurate positioning of the status of insulator defects [ 2 , 3 , 4 , 5 ] are important means to ensure the normal operation of transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

Liu,

Huang

2024

Sensors

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Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

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“…Thus, both daily inspections and long-term overhauls are mandatory for the safe and reliable functioning of the entire power grid system 3 . Unfortunately, traditional manual inspection has become obsolete due to its low efficiency and accuracy, 4 high cost and risks, 5 and heavy reliance on personal experience 6 . Other reasons for the decline of manual inspection include the rise of unmanned aerial vehicles 7 and advancements in artificial intelligence 8 …”

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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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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Technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection

Cited by 5 publications

References 15 publications

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

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

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