An insulator category detection method based on deep transfer learning

A technology of transfer learning and deep learning network, which is applied in the field of insulator category detection based on deep transfer learning, which can solve the problems of high misrecognition rate and poor generalization ability

Active Publication Date: 2021-03-09
HEFEI UNIV OF TECH
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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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  • An insulator category detection method based on deep transfer learning
  • An insulator category detection method based on deep transfer learning
  • An insulator category detection method based on deep transfer learning

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Embodiment Construction

[0068] In this embodiment, an insulator category detection method based on deep transfer learning is performed as follows:

[0069] Step 1. Preprocess the aerial insulator image set, check whether the computing power of the computer meets the calculation requirements of the original image size, whether there are problems such as jitter and blur, and perform size conversion, de-shaking and anti-noise processing, and obtain the pre-processed insulator image collection;

[0070] Step 2, expand the preprocessed aerial insulator image set, on the basis of the image processing in step 1, such as Figure 1a Shown is the original picture of the aerial insulator, Figure 1b Insulator mirror flip for aerial photography, Figure 1c Rotate +5° diagram for aerial photography of insulators, Figure 1d Rotate -5° diagram for aerial photography of insulators, Figure 1e Rotate +10° map for aerial photography of insulators; Figure 1f Rotate the -10° picture of the aerial insulator to simu...

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Abstract

The invention discloses an insulator category detection method based on deep transfer learning, comprising: 1. Preprocessing the aerial insulator image; 2. Expanding the preprocessed aerial insulator image and classifying different types of aerial insulator images ;3. Use the YOLO algorithm to initially locate the aerial insulator images with complex backgrounds, and perform normalization processing on the positioned insulators; 4. Construct an Inception deep learning network with a multi-level differential adaptive architecture; 5. Construct test samples 6. Construct an insulator state cognitive feedback adjustment mechanism based on semantic error entropy. Through the method of deep transfer learning, the present invention can realize the self-optimization adjustment and reconstruction of the multi-level differentiated feature space of the insulator state and its classification criteria, thereby improving the state detection rate of the self-explosion of the aerial image of the insulator under different backgrounds, and meeting the requirements of accuracy and speed actual needs.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/13G06V2201/07G06N3/045G06F18/214
Inventor 李帷韬焦点张倩丁美双
Owner HEFEI UNIV OF TECH
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