Recognition and Drop-Off Detection of Insulator Based on Aerial Image

Wang, Wanguo; Wang, Yufu; Han, Jun; Liu, Yue

doi:10.1109/iscid.2016.1045

Cited by 40 publications

(19 citation statements)

References 9 publications

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“…L cls is the classification loss function and L reg is the position regression loss function. The equations of L cls and L reg are shown in (4), (5). R in (5) is the robust loss function defined in (6).…”

Section: ) Region Proposal Networkmentioning

confidence: 99%

“…Zhang et al [3] adopted Hough transform [4] and statistical texture features to form feature sequence curves for defect detection of insulators. Wang et al [5] identified glass insulators and composite insulators by combining shape, color, and texture features, and diagnosed the glass insulator's off-chip defect. The algorithm for detecting objects by manual features has high customizability, and relatively, its versatility is poor.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning

2020

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The issues of existing research on transmission line detection include the following three: only detects a few categories, no open transmission line component dataset, and no unified, comprehensive evaluation index. In this paper, we propose a detection and evaluation method of defect for transmission line inspection based on deep learning. The transmission line contains various pivotal components, while previous research has mostly focused on a few categories. In the proposed approach, the following study is performed by establishing a transmission line dataset named Wire_10, which considers defects as a category. Wire_10 contains 8 defects in transmission line components, such as insulator defect, triple-plate defect, damper defect, grading ring defect, and et al., as well as nest and foreign body that attached to the transmission line. The object detection of aerial images taken during the actual inspection is susceptible to background and lighting. These two factors are used as variables to define the background-dataset and the lighting-dataset. Faster R-CNN, an end-to-end and high recognition accuracy deep learning algorithm, is used to build detection models with transfer learning and fine-tuning. The results show that the detection method can accurately identify the defect categories in the Wire_10 dataset and is robust to aerial images with complex backgrounds and different lighting. The proposed method can effectively and accurately identify defects in the automatic inspection of transmission lines. INDEX TERMS Transmission line defects, deep learning, aerial image, robustness.

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Section: ) Region Proposal Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning

2020

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“…Meanwhile, the increasing industrial needs have attracted more scholars for further research in the image recognition technology. For example, Wang et al in [5] propose an insulator defect-recognition method which integrates the shape, colour, and texture of insulators to reduce the influence of the background texture and illumination effectively. Lin et al in [6] conduct a faulty insulator diagnosis method for insulator detection based on repetitiveness feature from The associate editor coordinating the review of this manuscript and approving it for publication was Mingjian Cui .…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition Method of Electrical Components Based on YOLO V3

Chen

Shi

et al. 2019

IEEE Access

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The reliability of electrical components affects the stable operation of the power system. Electrical components inspection has long been important issues in the intelligent power system. The main problems of traditional recognition methods of electrical components are low detection accuracy and poor real-time performance, which are challenging to extract necessary features from the inspection images. This paper proposes a way to detect the electrical components in the Unmanned Aerial Vehicle (UAV) inspection image based on You Only Look Once (YOLO) V3 algorithm. Due to some of the inspection images are not clear, which result in the reduction of the available dataset. On this basis, we adopt Super-Resolution Convolutional Neural Network (SRCNN) to realize super-resolution reconstruction on the blurred image, which achieves the expansion of the dataset. We compare the performance of the proposed method with other popular recognition methods. The results of experiment verify the effectiveness of the proposed method, and the technique reaches high recognition accuracy, good robustness, and strong real-time performance for UAV power inspection system.

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“…(2) Detection method based on texture features. Zhang [12] and Wang [13] calculated the texture values of insulator images by block, and then analyzed whether the insulator was missing by comparing texture values. However, the calculation amounts of this method are very large.…”

Section: Introductionmentioning

confidence: 99%

Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features

et al. 2019

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During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings in the inspection images, when the umbrella skirts of the rear string are obstructed by the front string, defect detection becomes difficult. To solve this problem, we propose a method to detect self-shattering defects of insulators based on spatial features contained in images. Firstly, the images are segmented according to the particular color features of glass insulators, and the main axes of insulator strings in the images are adjusted to the horizontal direction. Then, the connected regions of insulators in the images are marked. After that, the vertical lengths of the regions, the number of insulator pixels in the regions, as well as the horizontal distances between two adjacent connected regions are selected as spatial features, based on which defect discriminants are formulated. Finally, experiments are performed using the proposed formula to detect self-shattering defects in the insulators, using the spatial distribution of the connected regions to locate the defects. The experiment results indicate that the proposed method has good detection accuracy and localization precision.

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Recognition and Drop-Off Detection of Insulator Based on Aerial Image

Cited by 40 publications

References 9 publications

Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning

Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning

Research on Recognition Method of Electrical Components Based on YOLO V3

Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features

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