Electric transmission line insulator burst identification method based on deep learning

Abstract

The invention discloses an electric transmission line insulator burst identification method based on deep learning. The method comprises the following steps that: firstly, collecting an electric transmission line insulator picture by an unmanned aerial vehicle; then, carrying out target detection on the electric transmission line insulator picture collected by the unmanned aerial vehicle so as toaccurately carry out regression to obtain the position of an insulator string in an original picture, and clipping an independent insulator string according to a position regression result; then, adopting a deep learning full-conventional neural network method to carry out semantic segmentation on the insulator string, and segmenting the insulator string from a background; and finally, extractingthe center of mass of the single insulator, solving a distance between the centers of mass of adjacent insulators, setting 1.5 times of average distance as a threshold value, considering that burst isin the presence in two insulators if the distance between two insulators is greater than the threshold value, and labeling an insulator burst position. By use of the method, a subjective influence brought by manually setting the threshold value and selecting the parameter in a traditional insulation extraction process is avoided, and identification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of deep learning and electric power identification, and in particular relates to a method for identifying a transmission line insulator burst. Background technique [0002] With the gradual popularization of drone applications in recent years, power line inspection drones have received extensive attention from major power grid companies and have broad application prospects. On the one hand, UAV line inspection has the characteristics of low risk, low cost and flexible operation in the field; on the other hand, the massive data generated needs to be manually interpreted to obtain the final inspection report, so the combination of deep learning and image recognition is adopted The method is of great significance to the identification of transmission line defects. [0003] Traditional transmission line insulator burst recognition algorithms mainly use artificially designed features, such as SIFT (Scale-invariant feature...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a deep learning-based insulator burst recognition method for transmission lines to solve the problems

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