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Rock specimen image intelligent identification method

A technology of intelligent identification and specimen, applied in the field of intelligent identification of rock specimen images, can solve the problem of low accuracy of rock identification

Pending Publication Date: 2022-07-05
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for intelligent recognition of rock specimen images, aiming to solve the problem of low accuracy of manual rock recognition

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  • Rock specimen image intelligent identification method
  • Rock specimen image intelligent identification method
  • Rock specimen image intelligent identification method

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Embodiment Construction

[0040] The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present invention and should not be construed as limiting the present invention.

[0041] see Figure 1 to Figure 6 , the present invention provides an intelligent recognition method for rock specimen images, comprising the following steps:

[0042] S1 constructs a rock specimen dataset;

[0043] The specific way is:

[0044] S11 Collect rock specimen pictures;

[0045] S12, classify and identify the rock specimen pictures according to the geological rock specimen division scheme, and obtain rock types;

[0046] Specifically, the identification criteria include the...

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Abstract

The invention relates to the technical field of image data processing, in particular to a rock specimen image intelligent identification method, which comprises the following steps: constructing a rock specimen data set; constructing a generative adversarial network model, and inputting the rock specimen data set into the generative adversarial network model for training to obtain an enhanced image data set; dividing the enhanced image data set into a training set and a test set; obtaining model parameters; constructing a recognition and classification model, and adjusting parameters of the recognition and classification model based on the model parameters to obtain an adjusted model; training the adjustment model by using the training set and the test set to obtain an optimal recognition and classification model; and inputting the to-be-detected rock specimen image into the optimal model to obtain an identification result, the rock is automatically identified by constructing the optimal model, the accuracy is high, and the problem of low accuracy of artificial rock identification is solved.

Description

technical field [0001] The invention relates to the technical field of image data processing, in particular to a method for intelligently identifying images of rock specimens. Background technique [0002] The rapid identification of lithology has important basic geological research significance and engineering application value. [0003] The traditional lithology identification methods are through manual direct observation, thin section identification, and experimental analysis. [0004] The traditional identification method using the above method has high requirements for manual identification experience and is greatly limited by hardware facilities. When faced with massive image data, image lithology identification cannot be completed quickly and accurately, which reduces the accuracy of rock identification. SUMMARY OF THE INVENTION [0005] The purpose of the present invention is to provide an intelligent recognition method for rock specimen images, which aims to solv...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/214
Inventor 欧发辉赖富强刘粤蛟蒋国强周欢焕张晓树刘源琦夏小雪闵宣霖谭先锋周伟
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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