Insulator category detection method based on deep transfer learning

Abstract

The invention discloses an insulator category detection method based on deep transfer learning. The insulator category detection method comprises the following steps: 1, preprocessing an aerial insulator image; 2, expanding the preprocessed aerial insulator images and classifying different types of aerial insulator images; 3, utilizing a YOLO algorithm to perform initial positioning on an aerial insulator image with a complex background, and performing normalization processing on the positioned insulator; 4, constructing an Inception deep learning network of a multi-level difference adaptive architecture; 5, constructing a classification result and a semantic error entropy of the test sample set; and 6, constructing an insulator state cognitive feedback adjustment mechanism based on semantic error entropy. According to the invention, through a deep transfer learning method, self-optimization adjustment and reconstruction of the insulator state multi-level differential feature space andthe classification criteria thereof can be realized, so that the self-explosion state detection rate of the aerial image of the insulator under different backgrounds is improved, and the actual demand of accuracy and rapidity is met.

Description

technical field [0001] The invention relates to the field of high-voltage transmission line inspection technology, image recognition technology, and transfer learning technology, in particular to an insulator category detection method based on deep transfer learning. Background technique [0002] With the continuous increase of people's production and living electricity demand around the world, the construction scale of the power grid is also expanding. The safety and reliability of transmission lines directly affect the stability of power transmission, and regular safety inspections are required , to eliminate potential failures. As an important part of fixed wires in overhead transmission lines, insulators are installed between conductors of different potentials or between conductors and grounding components, and are subjected to large mechanical tension and extremely high voltage. Therefore, self-explosion accidents often occur, seriously threatening the safe and reliabl...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the shortcomings of the above-mentioned prior art, the present invention proposes an insulator category detection method based on deep transfer learning, in order to solve the problems of poor generalization ability and high misrecognition rate in the open-loop non-feedback cognitive system , so as to imitate human beings to freely adjust the cognitive method for feedback cognition, improve the state detection accuracy of insulator aerial image self-explosion in complex backgrounds, and meet the actual needs of accuracy and speed

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