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Insulator identification method for unmanned aerial vehicle polling electric transmission line

A technology for insulator identification and transmission lines, applied in neural learning methods, character and pattern recognition, computer parts and other directions, can solve the problems of low detection accuracy, occlusion of slices and slices, and complex natural environment of transmission lines.

Active Publication Date: 2015-10-14
STATE GRID INTELLIGENCE TECH CO LTD
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AI Technical Summary

Problems solved by technology

Disadvantages: This method is seriously affected by light, and the natural environment of the transmission line is complex, with complex backgrounds such as trees, rivers, roads, etc., which makes the detection accuracy of this method not high
Disadvantages: Due to the problem of shooting angle, there is occlusion between the slices, resulting in detection errors

Method used

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  • Insulator identification method for unmanned aerial vehicle polling electric transmission line
  • Insulator identification method for unmanned aerial vehicle polling electric transmission line
  • Insulator identification method for unmanned aerial vehicle polling electric transmission line

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Embodiment Construction

[0063] The present invention is described in detail below in conjunction with accompanying drawing:

[0064] The training flow chart of the experimental process is as follows: Figure 6 As shown, the data set is extracted, the data set is packaged, the training process is started, the training hierarchy and initialization parameters are carried out before training, during training, forward training, reverse feedback, judging the maximum number of iterations / accuracy requirements, if yes, then output template, otherwise, continue training.

[0065] The flow chart of target recognition and positioning is as follows: Figure 7 As shown, input the image, initialize the detection model, extract the sub-image by the sliding window, and judge whether it is an insulator, if not, return to the process of extracting the sub-image by the sliding window, if it is, store the candidate frame, fit the candidate frame, mark the position of the original image, and output .

[0066] An effic...

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Abstract

The invention discloses an insulator identification method for an unmanned aerial vehicle polling an electric transmission line. The method comprises the steps: image collecting and processing: extracting sub-images for training from insulator images of the electric transmission line and initially processing the sub-images to form a training data set; packaging the extracted sub-images for training and adding labels corresponding to the sub-images; training the data by virtue of a convolutional neural network (CNN) algorithm in deep learning to obtain a detection model for insulators; insulator target region detection: detecting an electric transmission line image to obtain a candidate frame of the insulator target; carrying out non-maxima suppression on the candidate frame to obtain a final insulator candidate frame; and carrying out straight line fitting operation on the obtained final insulator candidate frame to obtain a central point and the angle and size information of the candidate frame and finally labeling on the insulator images of the electric transmission line. According to the method disclosed by the invention, images obtained by polling are screened, so that the burden of manual screening is alleviated and the method has a broad application prospect.

Description

technical field [0001] The invention relates to digital image processing and pattern recognition technology in the technical field of transmission line equipment detection, in particular to an efficient identification method for unmanned aerial vehicle inspection of transmission line insulators. Background technique [0002] Insulators are an important part of overhead transmission lines, used to support and fix busbars and live conductors, and to provide sufficient distance and insulation between live conductors or between conductors and the earth. Due to the long-term exposure of overhead transmission lines to the natural environment, affected by natural or human factors, there are problems such as line aging and damage. If these problems are not regularly inspected and repaired, major safety accidents may occur. [0003] Manual line inspection is inefficient and dangerous. With the development of UAV technology, the use of UAV aerial photography technology to collect hig...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/66G06N3/08
CPCG06N3/08G06V10/44G06V30/194
Inventor 刘越王万国刘俍张晶晶王滨海张方正雍军慕世友任杰傅孟潮魏传虎李建祥赵金龙
Owner STATE GRID INTELLIGENCE TECH CO LTD
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