Automatic recognition and classification of ore and mineral images

A technology of automatic identification and classification methods, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve the problem of less overall application, achieve the effects of eliminating subjectivity, improving work efficiency, and good robustness

Inactive Publication Date: 2019-01-29
SUN YAT SEN UNIV
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Problems solved by technology

However, in geological work, big data and artificial intelligence research is still in its infancy, and the overall application is still relatively small
How to build an automatic identification and classification system for rocks and minerals under the microscope based on the concept and technological progress of the current era of big data and artificial intelligence is still a scientific and technological problem that has not yet been well resolved

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  • Automatic recognition and classification of ore and mineral images
  • Automatic recognition and classification of ore and mineral images

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Embodiment Construction

[0023] The identification and classification method of the present invention comprises the following steps:

[0024] Step 1: Collect microscopic photos covering various types of rocks and minerals, and uniformly adjust image parameters and image segmentation and labeling;

[0025] Step 2: change the pixel position of the input photo to obtain more input image data, and realize image data increase processing;

[0026] Step 3: According to the image characteristics of rock minerals, use the Unet segmentation network model to learn the whole rock photos under the microscope according to the mineral types through the supervised training of the machine model. The specific explanation of the framework level is as follows:

[0027] Step1: In the input layer, directly input the original rock mineral image data into the network for training, and convert the original rock mineral image under the microscope into a feature image of 572*572 size;

[0028] Step2: The input layer propagate...

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Abstract

An automatic recognition and classification of ore and mineral images are disclosed in the invention. the invention utilizes the computer vision technology and the depth convolution neural network theory, Based on the big data platform Tensorflow, the convolution artificial neural network model is established, and the image data input model is trained according to the microscopic photographs of yellow iron ore from Jiapigou Gold Mine in Jilin Province, so as to realize the automatic recognition and classification of different ore minerals in the microscopic photographs of yellow iron ore. Theinvention can assist geologists to identify and classify the microscopic photographs of ore minerals and improve the working efficiency of geologists.

Description

technical field [0001] The invention specifically relates to a method for automatic recognition and classification of ore mineral images under a microscope based on deep learning. Background technique [0002] The identification and classification of rock minerals under the microscope is an indispensable link in geological work, and also plays a fundamental and guiding role in the entire geological work. Existing microscopic identification and classification methods of rock minerals use artificial means to make one-sided judgments on the structure, content and other lithofacies characteristics of rock minerals from several limited views in a thin section. However, in geological work, the research on big data and artificial intelligence is still in its infancy, and the overall application is still relatively small. How to build an automatic identification and classification system for rocks and minerals under a microscope based on the concept and technological progress of th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/34G06N3/04
CPCG06V10/267G06N3/045G06F18/214G06F18/241
Inventor 周永章徐述腾沈文杰张彦龙
Owner SUN YAT SEN UNIV
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