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Power transmission line image sample augmentation method based on adversarial network model

A network model and transmission line technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of limited number of transmission line image samples, low detection accuracy, insufficient number of image samples, etc.

Pending Publication Date: 2022-04-29
广西电网有限责任公司河池供电局
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Problems solved by technology

[0003] In the manual inspection method, the degree of manual participation is large, the efficiency is low, and the workload of personnel is also large, and the fault is not solved in time. With the development of science and technology, the use of drone inspection to detect defects in transmission lines has gradually replaced manual inspection. However, the number of image samples of transmission lines that can be collected by UAVs is limited, and the number of image samples is insufficient, which will lead to poor generalization ability and detection accuracy of the trained deep learning model. Advanced problems, it is difficult to meet the needs of practical applications

Method used

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  • Power transmission line image sample augmentation method based on adversarial network model
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  • Power transmission line image sample augmentation method based on adversarial network model

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Experimental program
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Effect test

Embodiment 1

[0027] A method for augmenting transmission line image samples based on an adversarial network model, comprising the following steps:

[0028] S101. Establishing an adversarial network model;

[0029] Build an adversarial network model.

[0030] S102. Acquire the image signal data of the transmission line, and convert the image signal data of the transmission line into digital image signal data;

[0031] Acquire the image signal data of the transmission line, the image signal data of the transmission line includes the image pixel distribution characteristic signal, the image color characteristic signal and the image brightness characteristic signal distribution; convert the image pixel distribution characteristic signal, the image color characteristic signal and the image brightness characteristic signal distribution into an image digitized signal data.

[0032] S103. Perform data preprocessing on the image digitized signal data, and generate a data preprocessing result;

...

Embodiment 2

[0068] The difference from Embodiment 1 is that the verification sample is input into the confrontation network model for verification, and if the comprehensive evaluation index does not meet the requirements, the confrontation network model is retrained; if the value of the FID evaluation index is greater than or equal to the set FID threshold, Or when the value of the IS evaluation index is less than or equal to the set IS threshold, the confrontational network model is retrained.

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Abstract

The invention discloses a power transmission line image sample augmentation method based on an adversarial network model. The method comprises the following steps: establishing the adversarial network model; acquiring image signal data of the power transmission line, and converting the image signal data of the power transmission line into image digital signal data; performing data preprocessing on the image digital signal data, and generating a data preprocessing result; performing stage training on an adversarial network model according to the data preprocessing result; a verification sample is input into the adversarial network model for verification, and if the comprehensive evaluation index meets the requirement, training is completed; and the image sample augmentation of the power transmission line is realized.

Description

technical field [0001] The invention relates to the technical field of power transmission lines, in particular to a method for augmenting transmission line image samples based on an adversarial network model. Background technique [0002] At present, the detection methods of transmission lines can be generally divided into traditional manual line inspection detection methods and detection methods using drone inspection and photography. [0003] In the manual inspection method, the degree of manual participation is large, the efficiency is low, and the workload of personnel is also large, and the fault is not solved in time. With the development of science and technology, the use of drone inspection to detect defects in transmission lines has gradually replaced manual inspection. However, the number of image samples of transmission lines that can be collected by UAVs is limited, and the number of image samples is insufficient, which will lead to poor generalization ability an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/13G06K9/62G06V10/764
CPCG06T7/13G06T2207/20081G06F18/241G06T5/73G06T5/70
Inventor 唐锦鹏王海霖邓春明石忠诚崖望洲谭堃黄东谭文海蒋英俊
Owner 广西电网有限责任公司河池供电局
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