The invention discloses an iInsulator self-explosion defect detection method based on an inspection image

Abstract

The invention provides an insulator self-explosion defect detection method based on an inspection image. The insulator self-explosion defect detection method based on the inspection image comprises the steps of acquiring that a to-be-detected inspection image which is shot by an electric power line inspection unmanned aerial vehicle and comprises a glass insulator is obtained, and manually markinga small number of insulator self-explosion defects are manually marked; gGenerating virtual inspection images containing insulator defects in batches in a simulation system; P; performing filtering enhancement processing on the real image and the virtual image to obtain an inspection image joint training set after filtering enhancement; T, training the Faster R-CNN-based deep learning network byusing the joint training set to obtain an insulator self-destruction defect detection modelraining based on Faster R-by using joint training set The CNN deep learning network obtains an insulator self-explosion defect detection model; and the detection model can detect the filtered and enhanced inspection image to be detected, and determine whether the inspection image comprising the glass insulator comprises a self-explosion defect or not. According to the method provided by the invention, the problem of insufficient quantity of big data training sets in previous deep learning training is solved; and the detection accuracy of the self-explosion defect of the insulator is improved.

Description

technical field [0001] The invention relates to the technical field of power grid operation and maintenance, and more specifically, to a method for detecting self-explosion defects of insulators based on inspection images. Background technique [0002] Glass insulators are widely used in power grid operation, and are the key components of drone inspections. The concentrated self-explosion of glass insulators may cause line trip failures and endanger the stable operation of the line. [0003] In recent years, power line inspection UAVs have been popularized and applied, and a large number of inspection images have been generated. Manually interpreting inspection images and detecting insulator self-explosion defects is inefficient, highly subjective, and poor in consistency, which greatly limits the efficiency of operation and maintenance work. However, the accuracy rate and missed detection rate of the existing methods for automatically detecting self-explosion defects of i...

AI Technical Summary

Problems solved by technology

[0004] The invention provides a detection method for insulator self-explosion defects based on inspection images to solve the problem that both the correct rate and the missed detection rate of the current insulator self-explosion defect detection methods cannot meet the application requirements

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