A small sample expansion method of inspection image data based on cgan

An image data and image data set technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low sample quality and inability to meet the accuracy of downstream model training, and achieve improved accuracy and robustness. sexual effect

Active Publication Date: 2022-06-21
GUANGDONG POWER GRID CORP ZHAOQING POWER SUPPLY BUREAU
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Problems solved by technology

[0003] Chinese patent CN109325532A, with a publication date of 2019.02.12, discloses an image processing method for expanding data sets with small samples. Although data expansion is achieved, the sample quality of the expanded data based on this method is low, which cannot meet the requirements of downstream model training. the accuracy of

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  • A small sample expansion method of inspection image data based on cgan
  • A small sample expansion method of inspection image data based on cgan
  • A small sample expansion method of inspection image data based on cgan

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[0070] The accompanying drawings are for illustrative purposes only, and should not be construed as limiting the present invention; in order to better illustrate the present embodiment, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable to the artisan that certain well-known structures and their descriptions may be omitted from the drawings. The positional relationships described in the drawings are only for exemplary illustration, and should not be construed as limiting the present invention.

[0071] like figure 1 As shown, a small sample expansion method for inspection image data based on CGAN includes the following steps:

[0072] Step 1. Collect inspection images, identify defect types corresponding to inspection images according to experience, and manually annotate images to obtain inspection image data sets; use anomaly detection algorithms t...

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Abstract

The invention relates to a CGAN-based small sample expansion method of inspection image data. First collect the inspection images, identify the defect types corresponding to the inspection images, and mark them, and use the abnormal detection algorithm to eliminate the abnormal images in the inspection image data set; then use the traditional image processing algorithm to clean the inspection image data set Perform preprocessing, and then use the inspection image data set to train the conditional generation confrontation network based on the convolutional neural network to obtain a CGAN model that can generate inspection image data for a given defect type; then use the trained CGAN model generator to sample A large amount of inspection image data is generated; according to the authenticity of the image output by the discriminator, the generated images whose authenticity is greater than a given threshold of authenticity are screened and added to the inspection image dataset to obtain an expanded inspection image dataset.

Description

technical field [0001] The invention belongs to the technical field of inspection image processing of power transmission equipment, and more particularly, relates to a small sample expansion method of inspection image data based on CGAN. Background technique [0002] The distribution network has a complex network structure, various types of equipment, a wide range of distribution points, a complex local environment, and a relatively poor security environment. The distribution network lines are increasingly dense and the inspection difficulty is increasing, but the timeliness and accuracy requirements of power inspection are constantly improving. The current manual inspection method is relatively simple, low efficiency, high security risk, and low defect detection rate. It is necessary to use the drone inspection method to improve the current inefficient inspection method, and at the same time combine deep learning and other technologies to quickly identify equipment defects...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/764G06K9/62G06N3/04G06N3/08G06V10/774
CPCG06N3/08G06N3/045G06F18/2411G06F18/214
Inventor 张谨立游林辉葛阳庾凌云胡峰孙仝陈政宋海龙黄达文王伟光梁铭聪黄志就谭子毅陈景尚李志鹏冯海林
Owner GUANGDONG POWER GRID CORP ZHAOQING POWER SUPPLY BUREAU
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